Free-association norms provide essential empirical data for investigating linguistic, semantic, and cultural phenomena in the cognitive sciences. Although large-scale norms exist for languages such as English, Dutch, Spanish, and Mandarin Chinese, no comparable resource has been available for German. To address this gap, we present free-association norms for 5,877 German cue words as part of the German version of the multilingual Small World of Words (SWOW) project. We describe the data collection procedures, participant characteristics, and our comprehensive preprocessing pipeline before introducing the resulting SWOW-DE data set. Using data from three established psycholinguistic paradigms, we show that SWOW-DE norms robustly predict performance in lexical decision tasks, relatedness judgments, and psycholinguistic word ratings. Furthermore, we demonstrate that SWOW-DE responses compare favorably with existing German resources and provide a preliminary cross-linguistic comparison revealing both shared and language-specific association patterns, highlighting promising directions for future research. Overall, SWOW-DE represents the largest collection of German free associations to date and offers a unique resource for linguistic, psychological, and cross-cultural research.

A basic task in first language acquisition likely involves discovering the boundaries between words or morphemes in input where these basic units are not overtly segmented. A number of unsupervised learning algorithms have been proposed in the last 20 years for these purposes, some of which have been implemented computationally, but whose results remain difficult to compare across papers. We created a tool that is open source, enables reproducible results, and encourages cumulative science in this domain. WordSeg has a modular architecture: It combines a set of corpora description routines, multiple algorithms varying in complexity and cognitive assumptions (including several that were not publicly available, or insufficiently documented), and a rich evaluation package. In the paper, we illustrate the use of this package by analyzing a corpus of child-directed speech in various ways, which further allows us to make recommendations for experimental design of follow-up work. Supplementary materials allow readers to reproduce every result in this paper, and detailed online instructions further enable them to go beyond what we have done. Moreover, the system can be installed within container software that ensures a stable and reliable environment. Finally, by virtue of its modular architecture and transparency, WordSeg can work as an open-source platform, to which other researchers can add their own segmentation algorithms.

The Child Language Data Exchange System (CHILDES) has played a critical role in research on child language development, particularly in characterizing the early language learning environment. Access to these data can be both complex for novices and difficult to automate for advanced users, however. To address these issues, we introduce childes-db, a database-formatted mirror of CHILDES that improves data accessibility and usability by offering novel interfaces, including browsable web applications and an R application programming interface (API). Along with versioned infrastructure that facilitates reproducibility of past analyses, these interfaces lower barriers to analyzing naturalistic parent–child language, allowing for a wider range of researchers in language and cognitive development to easily leverage CHILDES in their work.

We report on a psycholinguistic database of Chinese character handwriting based on a large-scale study that involved 203 participants, each handwriting 200 characters randomly sampled from a cohort of 1,600 characters. Apart from collecting writing latencies, durations, and accuracy, we also compiled 14 lexical variables for each character. Regressions showed that frequency, age of acquisition, and the word context (in which a character appears) are all-around and influential predictors of orthographic access (as reflected in writing latency), motor execution of handwriting (as reflected in writing duration), and accuracy. In addition, phonological factors (phonogram status, spelling regularity, and homophone density) impacted orthographic access but not handwriting execution. Semantic factors (imageability and concreteness) only affected accuracy. These results suggest, among other things, that phonology is consulted in orthographic access while handwriting. As the first of its kind, this database can be used as a source of secondary data analyses and a tool for stimulus construction in handwriting research.

Studies have shown that logographemes and radicals, subcharacter units in Chinese characters, are represented in the orthographic lexicon and are functional processing units in the writing of Chinese characters. Nevertheless, there is no consensus regarding how characters should be segmented into logographemes and radicals. This article reports handwriting data for a list of 209 Chinese characters (95 nonphonetic compounds and 114 phonetic compounds) in a copying task. To validate the constituent logographemes and radicals of the target Chinese characters, comparisons among between-radical interstroke intervals (ISIs), between-logographeme ISIs, and within-logographeme ISIs, as well as their interactions with orthographic factors including character frequency, stroke number, and configuration, were conducted using factorial analyses. The results showed that the ISI comparison method is effective in validating the constituent logographemes and radicals in Chinese characters. On the basis of this list of 209 stimuli, another 1,227 Chinese characters that share the same set of radicals with the stimuli were further identified. Their constituent logographemes were deduced accordingly. Altogether, the over 1,000 Chinese characters with validated constituent logographemes will serve as a powerful reference for future psycholinguistic and neurolinguistic research. Future potential applications are discussed.

While a reasonable amount of work has gone into automatically geoparsing text at the city or higher levels of granularity for different types of texts in different domains, there is relatively little research on geoparsing fine-grained locations such as buildings, green spaces and street names in text. This paper reports on how the Edinburgh Geoparser performs on this task for different types of literary text set in Edinburgh, the first UNESCO City of Literature. The non-copyrighted gold standard datasets created for this purpose are released along with this article.

Abstract Voice synthesis is a useful method for investigating the communicative role of different acoustic features. Although many text-to-speech systems are available, researchers of human nonverbal vocalizations and bioacousticians may profit from a dedicated simple tool for synthesizing and manipulating natural-sounding vocalizations. Soundgen (https://CRAN.R-project.org/package=soundgen) is an open-source R package that synthesizes nonverbal vocalizations based on meaningful acoustic parameters, which can be specified from the command line or in an interactive app. This tool was validated by comparing the perceived emotion, valence, arousal, and authenticity of 60 recorded human nonverbal vocalizations (screams, moans, laughs, and so on) and their approximate synthetic reproductions. Each synthetic sound was created by manually specifying only a small number of high-level control parameters, such as syllable length and a few anchors for the intonation contour. Nevertheless, the valence and arousal ratings of synthetic sounds were similar to those of the original recordings, and the authenticity ratings were comparable, maintaining parity with the originals for less complex vocalizations. Manipulating the precise acoustic characteristics of synthetic sounds may shed light on the salient predictors of emotion in the human voice. More generally, soundgen may prove useful for any studies that require precise control over the acoustic features of nonspeech sounds, including research on animal vocalizations and auditory perception.

We present a corpus of Finnish news articles with a manually prepared named entity annotation. The corpus consists of 953 articles (193,742 word tokens) with six named entity classes (organization, location, person, product, event, and date). The articles are extracted from the archives of Digitoday, a Finnish online technology news source. The corpus is available for research purposes. We present baseline experiments on the corpus using a rule-based and two deep learning systems on two, in-domain and out-of-domain, test sets.

Most words are ambiguous, with interpretation dependent on context. Advancing theories of ambiguity resolution is important for any general theory of language processing, and for resolving inconsistencies in observed ambiguity effects across experimental tasks. Focusing on homonyms (words such as bank with unrelated meanings EDGE OF A RIVER vs. FINANCIAL INSTITUTION), the present work advances theories and methods for estimating the relative frequency of their meanings, a factor that shapes observed ambiguity effects. We develop a new method for estimating meaning frequency based on the meaning of a homonym evoked in lines of movie and television subtitles according to human raters. We also replicate and extend a measure of meaning frequency derived from the classification of free associates. We evaluate the internal consistency of these measures, compare them to published estimates based on explicit ratings of each meaning’s frequency, and compare each set of norms in predicting performance in lexical and semantic decision mega-studies. All measures have high internal consistency and show agreement, but each is also associated with unique variance, which may be explained by integrating cognitive theories of memory with the demands of different experimental methodologies. To derive frequency estimates, we collected manual classifications of 533 homonyms over 50,000 lines of subtitles, and of 357 homonyms across over 5000 homonym–associate pairs. This database—publicly available at: www.blairarmstrong.net/homonymnorms/—constitutes a novel resource for computational cognitive modeling and computational linguistics, and we offer suggestions around good practices for its use in training and testing models on labeled data.

Ratings of body–object interaction (BOI) measure the ease with which the human body can interact with a word’s referent. Researchers have studied the effects of BOI in order to investigate the relationships between sensorimotor and cognitive processes. Such efforts could be improved, however, by the availability of more extensive BOI norms. In the present work, we collected BOI ratings for over 9,000 words. These new norms show good reliability and validity and have extensive overlap with the words used both in other lexical and semantic norms and in the available behavioral megastudies (e.g., the English Lexicon Project, Balota, Yap, Cortese, Hutchison, Kessler, & Loftis in Behavior Research Methods, 39, 445–459, 2007; and the Calgary Semantic Decision Project, Pexman, Heard, Lloyd, & Yap in Behavior Research Methods, 49, 407–417, 2017). In analyses using the new BOI norms, we found that high-BOI words tended to be more concrete, more graspable, and more strongly associated with sensory, haptic, and visual experience than are low-BOI words. When we used the new norms to predict response latencies and accuracy data from the behavioral megastudies, we found that BOI was a stronger predictor of responses in the semantic decision task than in the lexical decision task. These findings are consistent with a dynamic, multidimensional account of lexical semantics. The norms described here should be useful for future research examining the effects of sensorimotor experience on performance in tasks involving word stimuli.

Algeria’s socio-linguistic situation is known as a complex phenomenon involving several historical, cultural and technological factors. However, there are three languages that are mainly spoken in Algeria (Arabic, Tamazight and French) and they can be mixed in the same sentence (code-switching). Moreover, there are several varieties of dialects that differ from one region to another and sometimes within the same region. This paper aims to provide a new multi-purpose parallel corpus (i.e., DZDC12 corpus), which will serve as a testbed for various natural language processing and information retrieval applications. In particular, it can be a useful tool to study Arabic–French code-switching phenomenon, Algerian Romanized Arabic (Arabizi), different Algerian sub-dialects, sentiment analysis, gender writing style, machine translation, abuse detection, etc. To the best of our knowledge, the proposed corpus is the first of its kind, where the texts are written in Latin script and crawled from Facebook. More specifically, this corpus is organised by gender, region and city, and is transliterated into Arabic script and translated into Modern Standard Arabic. In addition, it is annotated for emotion detection and abuse detection, and annotated at the word level. This article focuses in particular on Algeria’s socio-linguistic situation and the effect of social media networks. Furthermore, the general guidelines for the design of DZDC12 corpus are described as well as the dialects clustering over the map.

Around the world, a growing interest has been seen in learner translator corpora, which are invaluable resources for teaching and research. This paper introduces a new resource to support researchers from different interdisciplinary areas such as computational linguistics, descriptive translation studies, computer-aided translation technology, Arabic machine translation applications, cognitive science, and translation pedagogy. Motivated by the lack of learner translator resources that provide data about learners of translation from and into Arabic, the undergraduate learner translator corpus (ULTC) is an ongoing, error-tagged sentence-aligned parallel corpus of English, Arabic, and French, with Arabic as its main language. The present corpus, consisting of parallel texts of female learners of translation from English or French into Arabic, is the first of its kind in terms of the languages represented, tasks covered, and number of students involved. It is also unique in terms of combining many complementary corpora of cross-lingual data, each of which has its own web-based query interface and corpus analysis tools. This paper describes the ULTC compilation process, preliminary findings, and planned future expansion and research.

The article details the formational process of the FinnTransFrame corpus, a part of the FinnFrameNet project. In addition to a large annotated frame semantic corpus of natural language examples, the project created a separate corpus of examples translated from English to Finnish. The research question when creating the FinnTransFrame corpus was to see to what extent the various frames of the original Berkeley FrameNet transfer into Finnish in translated examples, i.e. what are the main problems and how can they be categorized? A variety of Berkeley FrameNet examples were chosen from different frames and then translated by professionals. The FinnFrameNet annotation team checked all the examples and their translations to see if the frames remained intact in translation. Problematic examples were tagged according to the type of the encountered problem, with the main focus on the type of fine-grained mismatches of meaning that caused frame changes even when the translation was the best possible one. The frame-loss amounted to 4.2% of the 88,209 relevant example sentences. Filtering out sentences with other types of problems, we found that 88.1% of all the frame instances still translated into Finnish with their frame intact. In addition, the article analyzes the error types in the problematic frames.

Spoken corpora are important for speech research, but are expensive to create and do not necessarily reflect (read or spontaneous) speech ‘in the wild’. We report on our conversion of the preexisting and freely available Spoken Wikipedia into a speech resource. The Spoken Wikipedia project unites volunteer readers of Wikipedia articles. There are initiatives to create and sustain Spoken Wikipedia versions in many languages and hence the available data grows over time. Thousands of spoken articles are available to users who prefer a spoken over the written version. We turn these semi-structured collections into structured and time-aligned corpora, keeping the exact correspondence with the original hypertext as well as all available metadata. Thus, we make the Spoken Wikipedia accessible for sustainable research. We present our open-source software pipeline that downloads, extracts, normalizes and text–speech aligns the Spoken Wikipedia. Additional language versions can be exploited by adapting configuration files or extending the software if necessary for language peculiarities. We also present and analyze the resulting corpora for German, English, and Dutch, which presently total 1005 h and grow at an estimated 87 h per year. The corpora, together with our software, are available via http://islrn.org/resources/684-927-624-257-3/. As a prototype usage of the time-aligned corpus, we describe an experiment about the preferred modalities for interacting with information-rich read-out hypertext. We find alignments to help improve user experience and factual information access by enabling targeted interaction.

Advances in emotional speech recognition and synthesis essentially rely on the availability of annotated emotional speech corpora. As a low resource language, the Thai language critically lacks corpora of emotional speech, although a few corpora have been constructed for speech recognition and synthesis. This paper presents the design of a Thai emotional speech corpus (namely EMOLA), its construction and annotation process, and its analysis. In the corpus design, four basic types with twelve subtypes of emotions are defined with consideration of the Pleasure-Arousal-Dominance emotional state model. To construct the corpus, a series of Thai dramas (1397 min) were selected and its video clips of approximately 868 min were annotated. As a result, 8987 transcriptions (of conversation turns) were derived in total, with each transcription tagged as one basic type and a few subtypes. Finally, an analysis was conducted to describe the characteristics of this corpus in three sets of statistics: collection-level, annotator-oriented and actor-oriented statistics.

The Large Database of English Compounds (LADEC) consists of over 8,000 English words that can be parsed into two constituents that are free morphemes, making it the largest existing database specifically for use in research on compound words. Both monomorphemic (e.g., wheel) and multimorphemic (e.g., teacher) constituents were used. The items were selected from a range of sources, including CELEX, the English Lexicon Project, the British Lexicon Project, the British National Corpus, and Wordnet, and were hand-coded as compounds (e.g., snowball). Participants rated each compound in terms of how predictable its meaning is from its parts, as well as the extent to which each constituent retains its meaning in the compound. In addition, we obtained linguistic characteristics that might influence compound processing (e.g., frequency, family size, and bigram frequency). To show the usefulness of the database in investigating compound processing, we conducted a number of analyses that showed that compound processing is consistently affected by semantic transparency, as well as by many of the other variables included in LADEC. We also showed that the effects of the variables associated with the two constituents are not symmetric. In short, LADEC provides the opportunity for researchers to investigate a number of questions about compounds that have not been possible to investigate in the past, due to the lack of sufficiently large and robust datasets. In addition to directly allowing researchers to test hypotheses using the information included in LADEC, the database will contribute to future compound research by allowing better stimulus selection and matching.

In the domain of cognitive studies on the lexico-semantic representational system, one of the most important means of ensuring effective experimental designs is using ecological stimulus sets accompanied by normative data on the most relevant variables affecting the processing of their items. In the context of image sets, color photographs are particularly suited to this purpose as they reduce the difficulty of visual decoding processes that may emerge with traditional image sets of line drawings. This is especially so in clinical populations. In this study we provide Italian norms for a set of 357 high quality image-items belonging to 23 semantic subcategories from the Moreno-Martínez and Montoro database. Data from several variables affecting image processing were collected from a sample of 255 Italian-speaking participants: age of acquisition, familiarity, lexical frequency, manipulability, name agreement, typicality and visual complexity. Lexical frequency data were derived from t)

The aim of word sense disambiguation (WSD) is to correctly identify the meaning of a word in context. All natural languages exhibit word sense ambiguities and these are often hard to resolve automatically. Consequently WSD is considered an important problem in natural language processing (NLP). Standard evaluation resources are needed to develop, evaluate and compare WSD methods. A range of initiatives have lead to the development of benchmark WSD corpora for a wide range of languages from various language families. However, there is a lack of benchmark WSD corpora for South Asian languages including Urdu, despite there being over 300 million Urdu speakers and a large amounts of Urdu digital text available online. To address that gap, this study describes a novel benchmark corpus for the Urdu Lexical Sample WSD task. This corpus contains 50 target words (30 nouns, 11 adjectives, and 9 verbs). A standard, manually crafted dictionary called Urdu Lughat is used as a sense inventory. Four baseline WSD approaches were applied to the corpus. The results show that the best performance was obtained using a simple Bag of Words approach. To encourage NLP research on the Urdu language the corpus is freely available to the research community.

This paper introduces Emilia, a speech corpus created to build a female voice in Spanish spoken in Buenos Aires for the Aromo text-to-speech system. Aromo is a unit selection text-to-speech system, which employs diphones as units of synthesis. The key requirements and design criteria for Emilia were: to synthesize any text in Spanish into high-quality speech with a minimum corpus size. The text corpus was designed to guarantee the phonetic and prosodic coverage. A three-stage strategy was used: in the first stage, 741 sentences were designed with all of the syllables of Spanish spoken in Argentina, with and without stress, and in all positions within the word; in the second stage, 852 sentences were added to balance out the distribution of the diphones; and after a perceptual evaluation of the quality of synthesized speech, in the third and final stage, 625 sentences were added to achieve the specified unit coverage, and to introduce sentences with more complex syntactic and prosodic structures. Issues from all three corpus building stages are reported. The paper also presents the results from the quality perceptual evaluations of the synthesized voice. Emilia has a duration of three hours and 15 minutes; its speech quality synthesized with Aromo system is similar to the level obtained with commercial systems, with a real-time ratio less than one.

This article describes the procedures employed during the development of the first comprehensive machine-readable Turkish Sign Language (TiD) resource: a bilingual lexical database and a parallel corpus between Turkish and TiD. In addition to sign language specific annotations (such as non-manual markers, classifiers and buoys) following the recently introduced TiD knowledge representation (Eryiğit et al. 2016), the parallel corpus contains also annotations of dependency relations, which makes it the first parallel treebank between a sign language and an auditory-vocal language.

The LENA system has revolutionized research on language acquisition, providing both a wearable device to collect day-long recordings of children’s environments, and a set of automated outputs that process, identify, and classify speech using proprietary algorithms. This output includes information about input sources (e.g., adult male, electronics). While this system has been tested across a variety of settings, here we delve deeper into validating the accuracy and reliability of LENA’s automated diarization, i.e., tags of who is talking. Specifically, we compare LENA’s output with a gold standard set of manually generated talker tags from a dataset of 88 day-long recordings, taken from 44 infants at 6 and 7 months, which includes 57,983 utterances. We compare accuracy across a range of classifications from the original Lena Technical Report, alongside a set of analyses examining classification accuracy by utterance type (e.g., declarative, singing). Consistent with previous validations, we find overall high agreement between the human and LENA-generated speaker tags for adult speech in particular, with poorer performance identifying child, overlap, noise, and electronic speech (accuracy range across all measures: 0–92%). We discuss several clear benefits of using this automated system alongside potential caveats based on the error patterns we observe, concluding with implications for research using LENA-generated speaker tags.

Language is one the earliest capacities affected by cognitive change. To monitor that change longitudinally, we have developed a web portal for remote linguistic data acquisition, called Talk2Me, consisting of a variety of tasks. In order to facilitate research in different aspects of language, we provide baselines including the relations between different scoring functions within and across tasks. These data can be used to augment studies that require a normative model; for example, we provide baseline classification results in identifying dementia. These data are released publicly along with a comprehensive open-source package for extracting approximately two thousand lexico-syntactic, acoustic, and semantic features. This package can be applied arbitrarily to studies that include linguistic data. To our knowledge, this is the most comprehensive publicly available software for extracting linguistic features. The software includes scoring functions for different tasks. [ABSTRACT FROM)

We present a new set of subjective Age of Acquisition (AoA) ratings for 299 words (158 nouns, 141 verbs) in seven languages from various language families and cultural settings: American English, Czech, Scottish Gaelic, Lebanese Arabic, Malaysian Malay, Persian, and Western Armenian. The ratings were collected from a total of 173 participants and were highly reliable in each language. We applied the same method of data collection as used in a previous study on 25 languages which allowed us to create a database of fully comparable AoA ratings of 299 words in 32 languages. We found that in the seven languages not included in the previous study, the words are estimated to be acquired at roughly the same age as in the previously reported languages, i.e. mostly between the ages of 1 and 7 years. We also found that the order of word acquisition is moderately to highly correlated across all 32 languages, which extends our previous conclusion that early words are acquired in similar order acr)

The Moral Foundations Dictionary (MFD) is a useful tool for applying the conceptual framework developed in Moral Foundations Theory and quantifying the moral meanings implicated in the linguistic information people convey. However, the applicability of the MFD is limited because it is available only in English. Translated versions of the MFD are therefore needed to study morality across various cultures, including non-Western cultures. The contribution of this paper is two-fold. We developed the first Japanese version of the MFD (referred to as the J-MFD) using a semi-automated method—this serves as a reference when translating the MFD into other languages. We next tested the validity of the J-MFD by analyzing open-ended written texts about the situations that Japanese participants thought followed and violated the five moral foundations. We found that the J-MFD correctly categorized the Japanese participants’ descriptions into the corresponding moral foundations, and that the Moral F)

We introduce a dataset for studying the evolution of words, constructed from WordNet and the Google Books Ngram Corpus. The dataset tracks the evolution of 4,000 synonym sets (synsets), containing 9,000 English words, from 1800 AD to 2000 AD. We present a supervised learning algorithm that is able to predict the future leader of a synset: the word in the synset that will have the highest frequency. The algorithm uses features based on a word’s length, the characters in the word, and the historical frequencies of the word. It can predict change of leadership (including the identity of the new leader) fifty years in the future, with an F-score considerably above random guessing. Analysis of the learned models provides insight into the causes of change in the leader of a synset. The algorithm confirms observations linguists have made, such as the trend to replace the -ise suffix with -ize, the rivalry between the -ity and -ness suffixes, and the struggle between economy (shorter words ar)

We present the design and development of a South African directory enquiries corpus. It contains audio and orthographic transcriptions of a wide range of South African names produced by first-language speakers of four languages, namely Afrikaans, English, isiZulu and Sesotho. Useful as a resource to understand the effect of name language and speaker language on pronunciation, this is the first corpus to also aim to identify the “intended language”: an implicit assumption with regard to word origin made by the speaker of the name. We describe the design, collection, annotation, and verification of the corpus. This includes an analysis of the algorithms used to tag the corpus with meta information that may be beneficial to pronunciation modelling tasks.

Ongoing advances in computer technology have opened up a deluge of new datasets for understanding human behavior (Goldstone & Lupyan, 2016). Many of these datasets provide information on the use of written language. However, data on naturally occurring spoken-language conversations are much more difficult to obtain. A major exception to this is the TalkBank system, which provides online multimedia data for 14 types of spoken-language data: language in aphasia, child language, stuttering, child phonology, autism spectrum disorder, bilingualism, Conversation Analysis, classroom discourse, dementia, right hemisphere damage, Danish conversation, second language learning, traumatic brain injury, and daylong recordings in the home. The present report reviews these resources and describes the ways they are being used to further our understanding of human language and communication.

The application of word associations has become increasingly widespread. However, the association norms produced by traditional free association tests tend not to exceed 10,000 stimulus words, making the number of associated words too small to be representative of the overall language. In this study we used text corpora totaling over 400 million Chinese words, along with a multitude of association measures, to automatically construct a Chinese Lexical Association Database (CLAD) comprising the lexical association of over 80,000 words. Comparison of the CLAD with a database of traditional Chinese word association norms shows that word associations extracted from large text corpora are similar in strength to those elicited from free association tests but contain a much greater number of associative word pairs. Additionally, the relatively small numbers of participants involved in the creation of traditional norms result in relatively coarse scales of association measurement, whereas the differentiation of association strengths is greatly enhanced in the CLAD. The CLAD provides researchers with a great supplement to traditional word association norms. A query website at www.chinesereadability.net/LexicalAssociation/CLAD/ affords access to the database.

Embodiment theory suggests that, during the processing of words related to movement, as in the case of action verbs, somatotopic activation is produced in the motor and premotor cortices. In the same way, some studies have demonstrated that patients with frontal-lobe damage, such as Parkinson’s patients, have difficulties processing that kind of stimulus. At the moment, no standardized data exist concerning the motor content of Spanish verbs. Therefore, the aim of the present research was to develop a database of 4,565 verbs in Spanish through a survey filled out by 152 university students. The value for the motor content was obtained by calculating the average value from the answers of the participants. In addition, the reliability of the results was estimated, as well as their convergent validity, using diverse correlation coefficients. The database and the raw responses of the participants can be downloaded from this website: https://inco.grupos.uniovi.es/enlaces.

Automatic term extraction is a productive field of research within natural language processing, but it still faces significant obstacles regarding datasets and evaluation, which require manual term annotation. This is an arduous task, made even more difficult by the lack of a clear distinction between terms and general language, which results in low inter-annotator agreement. There is a large need for well-documented, manually validated datasets, especially in the rising field of multilingual term extraction from comparable corpora, which presents a unique new set of challenges. In this paper, a new approach is presented for both monolingual and multilingual term annotation in comparable corpora. The detailed guidelines with different term labels, the domain- and language-independent methodology and the large volumes annotated in three different languages and four different domains make this a rich resource. The resulting datasets are not just suited for evaluation purposes but can also serve as a general source of information about terms and even as training data for supervised methods. Moreover, the gold standard for multilingual term extraction from comparable corpora contains information about term variants and translation equivalents, which allows an in-depth, nuanced evaluation.

Word associations have been used widely in psychology, but the validity of their application strongly depends on the number of cues included in the study and the extent to which they probe all associations known by an individual. In this work, we address both issues by introducing a new English word association dataset. We describe the collection of word associations for over 12,000 cue words, currently the largest such English-language resource in the world. Our procedure allowed subjects to provide multiple responses for each cue, which permits us to measure weak associations. We evaluate the utility of the dataset in several different contexts, including lexical decision and semantic categorization. We also show that measures based on a mechanism of spreading activation derived from this new resource are highly predictive of direct judgments of similarity. Finally, a comparison with existing English word association sets further highlights systematic improvements provided through these new norms.

The research of the word is still very much the research of the noun. Adjectives have been largely overlooked, despite being the second-largest word class in many languages and serving an important communicative function, because of the rich, nuanced qualifications they afford. Adjectives are also ideally suited to study the interface between cognition and emotion, as they naturally cover the entire range of lexicosemantic variables such as imageability (infinite–green), and affective variables such as valence (sad–happy). We illustrate this by showing how the centrality of words in the mental lexicon varies as a function of the words’ affective dimensions, using newly collected norms for 1,000 Dutch adjectives. The norms include the lexicosemantic variables age of acquisition, familiarity, concreteness, and imageability; the affective variables valence, arousal, and dominance; and a variety of distributional variables, including network statistics resulting from a large-scale word association study. The norms are freely available from https://osf.io/nyg8v/, for researchers studying adjectives specifically or for whom adjectives constitute convenient stimuli to study other topics, such as vagueness, inference, spatial cognition, or affective word processing.

Here we describe the Jena Speaker Set (JESS), a free database for unfamiliar adult voice stimuli, comprising voices from 61 young (18–25 years) and 59 old (60–81 years) female and male speakers uttering various sentences, syllables, read text, semi-spontaneous speech, and vowels. Listeners rated two voice samples (short sentences) per speaker for attractiveness, likeability, two measures of distinctiveness (“deviation”-based [DEV] and “voice in the crowd”-based [VITC]), regional accent, and age. Interrater reliability was high, with Cronbach’s α between .82 and .99. Young voices were generally rated as more attractive than old voices, but particularly so when male listeners judged female voices. Moreover, young female voices were rated as more likeable than both young male and old female voices. Young voices were judged to be less distinctive than old voices according to the DEV measure, with no differences in the VITC measure. In age ratings, listeners almost perfectly discriminated young from old voices; additionally, young female voices were perceived as being younger than young male voices. Correlations between the rating dimensions above demonstrated (among other things) that DEV-based distinctiveness was strongly negatively correlated with rated attractiveness and likeability. By contrast, VITC-based distinctiveness was uncorrelated with rated attractiveness and likeability in young voices, although a moderate negative correlation was observed for old voices. Overall, the present results demonstrate systematic effects of vocal age and gender on impressions based on the voice and inform as to the selection of suitable voice stimuli for further research into voice perception, learning, and memory.

We present a new dataset of English word recognition times for a total of 62 thousand words, called the English Crowdsourcing Project. The data were collected via an internet vocabulary test in which more than one million people participated. The present dataset is limited to native English speakers. Participants were asked to indicate which words they knew. Their response times were registered, although at no point were the participants asked to respond as quickly as possible. Still, the response times correlate around .75 with the response times of the English Lexicon Project for the shared words. Also, the results of virtual experiments indicate that the new response times are a valid addition to the English Lexicon Project. This not only means that we have useful response times for some 35 thousand extra words, but we now also have data on differences in response latencies as a function of education and age.

This article presents the development of the “Hoosier Vocal Emotions Corpus,” a stimulus set of recorded pseudo-words based on the pronunciation rules of English. The corpus contains 73 controlled audio pseudo-words uttered by two actresses in five different emotions (i.e., happiness, sadness, fear, anger, and disgust) and in a neutral tone, yielding 1,763 audio files. In this article, we describe the corpus as well as a validation study of the pseudo-words. A total of 96 native English speakers completed a forced choice emotion identification task. All emotions were recognized better than chance overall, with substantial variability among the different tokens. All of the recordings, including the ambiguous stimuli, are made freely available, and the recognition rates and the full confusion matrices for each stimulus are provided in order to assist researchers and clinicians in the selection of stimuli. The corpus has unique characteristics that can be useful for experimental paradigms that require controlled stimuli (e.g., electroencephalographic or fMRI studies). Stimuli from this corpus could be used by researchers and clinicians to answer a variety of questions, including investigations of emotion processing in individuals with certain temperamental or behavioral characteristics associated with difficulties in emotion recognition (e.g., individuals with psychopathic traits); in bilingual individuals or nonnative English speakers; in patients with aphasia, schizophrenia, or other mental health disorders (e.g., depression); or in training automatic emotion recognition algorithms. The Hoosier Vocal Emotions Corpus is available at https://psycholinguistics.indiana.edu/hoosiervocalemotions.htm.

Studies on morphological processing in French, as in other languages, have shown disparate results. We argue that a critical and long-overlooked factor that could underlie these diverging results is the methodological differences in the calculation of morphological variables across studies. To address the need for a common morphological database, we present MorphoLex-FR, a sizeable and freely available database with 12 variables for prefixes, roots, and suffixes for the 38,840 words of the French Lexicon Project. MorphoLex-FR constitutes a first step to render future studies addressing morphological processing in French comparable. The procedure we used for morphological segmentation and variable computation is effectively the same as that in MorphoLex, an English morphological database. This will allow for cross-linguistic comparisons of future studies in French and English that will contribute to our understanding of how morphologically complex words are processed. To validate these variables, we explored their influence on lexical decision latencies for morphologically complex nouns in a series of hierarchical regression models. The results indicated that only morphological variables related to the suffix explained lexical decision latencies. The frequency and family size of the suffix exerted facilitatory effects, whereas the percentage of more frequent words in the morphological family of the suffix was inhibitory. Our results are in line with previous studies conducted in French and in English. In conclusion, this database represents a valuable resource for studies on the effect of morphology in visual word processing in French.

The paper describes the conversion of an LFG treebank of Polish into enhanced Universal Dependencies, and—more generally—identifies the kinds of information lost in translation from LFG to UD. The paper also presents the resulting UD treebank of Polish and compares it to the previous UD treebank of Polish.

The Glasgow Norms are a set of normative ratings for 5,553 English words on nine psycholinguistic dimensions: arousal, valence, dominance, concreteness, imageability, familiarity, age of acquisition, semantic size, and gender association. The Glasgow Norms are unique in several respects. First, the corpus itself is relatively large, while simultaneously providing norms across a substantial number of lexical dimensions. Second, for any given subset of words, the same participants provided ratings across all nine dimensions (33 participants/word, on average). Third, two novel dimensions—semantic size and gender association—are included. Finally, the corpus contains a set of 379 ambiguous words that are presented either alone (e.g., toast) or with information that selects an alternative sense (e.g., toast (bread), toast (speech)). The relationships between the dimensions of the Glasgow Norms were initially investigated by assessing their correlations. In addition, a principal component analysis revealed four main factors, accounting for 82% of the variance (Visualization, Emotion, Salience, and Exposure). The validity of the Glasgow Norms was established via comparisons of our ratings to 18 different sets of current psycholinguistic norms. The dimension of size was tested with megastudy data, confirming findings from past studies that have explicitly examined this variable. Alternative senses of ambiguous words (i.e., disambiguated forms), when discordant on a given dimension, seemingly led to appropriately distinct ratings. Informal comparisons between the ratings of ambiguous words and of their alternative senses showed different patterns that likely depended on several factors (the number of senses, their relative strengths, and the rating scales themselves). Overall, the Glasgow Norms provide a valuable resource—in particular, for researchers investigating the role of word recognition in language comprehension.

This paper introduces a large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO). The database includes 3000 semi-natural utterances, equivalent to 3 h and 25 min of speech data extracted from online radio plays. The ShEMO covers speech samples of 87 native-Persian speakers for five basic emotions including anger, fear, happiness, sadness and surprise, as well as neutral state. Twelve annotators label the underlying emotional state of utterances and majority voting is used to decide on the final labels. According to the kappa measure, the inter-annotator agreement is 64% which is interpreted as “substantial agreement”. We also present benchmark results based on common classification methods in speech emotion detection task. According to the experiments, support vector machine achieves the best results for both gender-independent (58.2%) and gender-dependent models (female = 59.4%, male = 57.6%). The ShEMO will be available for academic purposes free of charge to provide a baseline for further research on Persian emotional speech.

This paper presents the DialogBank, a new language resource consisting of dialogues with gold standard annotations according to the ISO 24617-2 standard. Some of these dialogues have been taken from existing corpora and have been re-annotated, offering the possibility to compare annotations according to different schemes; others have been newly annotated directly according to the standard. The ISO standard annotations in the DialogBank make use of three alternative representation formats, which are shown to be interoperable. The (re-)annotation brought certain deficiencies and limitations of the ISO standard to light, which call for considering possible revisions and extensions, and for exploring the possible integration of dialogue act annotations with other semantic annotations.

Compared to early language development, later changes to the language system during orthography and literacy acquisition have not yet been researched in detail. We present a longitudinal corpus of texts on short picture stories written by German primary school children between grades 2 and 4 and grades 3 and 4. It includes 1,922 texts with 212,505 tokens (6,364 types) from 251 children. For each text, rich metadata is available, including age, grade and linguistic background (at least 60% of the children were multilingual). To our knowledge, our corpus is the largest longitudinal corpus of written texts by children at primary school age. Each word is included in its original spelling as well as in a normalized form (target hypothesis), specifying the intended word form, which we corrected for orthographic but not grammatical errors. Original and target word forms are aligned character-wise and the target word forms are enriched with phonological, syllabic, and morphological information. Additionally, for each target word form, we established key lexical variables, e.g., word frequency or summed bigram frequency, as specified in childLex. Where applicable, we also specify key features of German orthography (e.g., consonant doubling, vowel-lengthening <h>). Taken together, this information allows for a detailed assessment of the properties of words that tend to increase the likelihood of spelling errors. The corpus is available in different formats—as tab-delimited annotated token and type based lists, in an XML format, and via the corpus search tool ANNIS.

Modality exclusivity norms have been developed in different languages for research on the relationship between perceptual and conceptual systems. This paper sets up the first modality exclusivity norms for Chinese, a Sino-Tibetan language with semantics as its orthographically relevant level. The norms are collected through two studies based on Chinese sensory words. The experimental designs take into consideration the morpho-lexical and orthographic structures of Chinese. Study 1 provides a set of norms for Mandarin Chinese single-morpheme words in mean ratings of the extent to which a word is experienced through the five sense modalities. The degrees of modality exclusivity are also provided. The collected norms are further analyzed to examine how sub-lexical orthographic representations of sense modalities in Chinese characters affect speakers’ interpretation of the sensory words. In particular, we found higher modality exclusivity rating for the sense modality explicitly represent)

Corpus-based research has formed the backbone of linguistic research in recent decades. Large text corpora are used for solving various kinds of linguistic problems, including those of quantitative linguistics, cognitive linguistics, and psycholinguistics. This paper reports the creation of two corpora of contemporary Vietnamese. It also describes the construction of these two equally sized Vietnamese corpora (a corpus from Vietnamese film subtitles, subtlex-viet, and a general corpus of varieties of online newspapers and stories, genlex-viet). We document the general steps of the construction and extraction of linguistic information from the language corpora and provide a road map for others who would like to create similar corpora. The resultant corpora are available in three versions: plain text, tokenized, and POS tagged. In the second half of the paper, the construction of a lexical database derived from the corpora is described. The database includes measures such as frequency of occurrence, dispersion, Mutual Information, Inverse Document Frequency, as well as vector space measures based on Latent Semantic Analysis and Hyperspace Analogue to Language. We conclude by reporting a comparison of the lexical predictors and a validation using psycholinguistic data from visual lexical decision experiments.

Vowels in Arabic are optional orthographic symbols written as diacritics above or below letters. In Arabic texts, typically more than 97 percent of written words do not explicitly show any of the vowels they contain; that is to say, depending on the author, genre and field, less than 3 percent of words include any explicit vowel. Although numerous studies have been published on the issue of restoring the omitted vowels in speech technologies, little attention has been given to this problem in papers dedicated to written Arabic technologies. In this research, we present Arabic-Unitex, an Arabic Language Resource, with emphasis on vowel representation and encoding. Specifically, we present two dozens of rules formalizing a detailed description of vowel omission in written text. They are typographical rules integrated into large-coverage resources for morphological annotation. For restoring vowels, our resources are capable of identifying words in which the vowels are not shown, as well as words in which the vowels are partially or fully included. By taking into account these rules, our resources are able to compute and restore for each word form a list of compatible fully vowelized candidates through omission-tolerant dictionary lookup. In our previous studies, we have proposed a straightforward encoding of taxonomy for verbs (Neme in Proceedings of the international workshop on lexical resources (WoLeR) at ESSLLI, 2011) and broken plurals (Neme and Laporte in Lang Sci, 2013, http://dx.doi.org/10.1016/j.langsci.2013.06.002). While traditional morphology is based on derivational rules, our description is based on inflectional ones. The breakthrough lies in the reversal of the traditional root-and-pattern Semitic model into pattern-and-root, giving precedence to patterns over roots. The lexicon is built and updated manually and contains 76,000 fully vowelized lemmas. It is then inflected by means of finite-state transducers (FSTs), generating 6 million forms. The coverage of these inflected forms is extended by formalized grammars, which accurately describe agglutinations around a core verb, noun, adjective or preposition. A laptop needs one minute to generate the 6 million inflected forms in a 340-MB flat file, which is compressed in 2 min into 11 MB for fast retrieval. Our program performs the analysis of 5000 words/second for running text (20 pages/second). Based on these comprehensive linguistic resources, we created a spell checker that detects any invalid/misplaced vowel in a fully or partially vowelized form. Finally, our resources provide a lexical coverage of more than 99 percent of the words used in popular newspapers, and restore vowels in words (out of context) simply and efficiently.

What are known as specialized or specialist dictionaries are much more than lists of words and their definitions with occasional comments on things such as synonymy and homonymy. That is to say, a particular specialist term may be associated with many other concepts, including quotations, different senses, etymological categories, semantic categories, superordinate and subordinate terms in the terminological hierarchy, spelling variants, and references to background sources discussing the exact meaning and application of the term. The various concepts, in turn, form networks of mutual links, which makes the structure of the background concepts demanding to model when designing a database structure for this type of dictionary. The Dictionary of medical vocabulary in English, 1375–1550 is a specialized historical dictionary that covers the vast medical lexicon of the centuries examined. It comprises over 12,000 terms, each of them associated with a host of background concepts. Compiling the dictionary took over 15 years. The process started with an analysis of hand-written manuscripts and early printed books from different sources and ended with the electronic dictionary described in the present paper. Over these years, the conceptual structure, database schema, and requirements for essential use cases were iteratively developed. In our paper, we introduce the conceptual structure and database schema modelled for implementing an electronic dictionary that involves different use cases such as term insertion and linking a term to related concepts. The achieved conceptual model, database structure, and use cases provide a general framework for reference-oriented specialized dictionaries, including ones with a historical orientation.

Corpora play an important role when training machine learning systems for sentiment analysis. However, Spanish is underrepresented in these corpora, as most primarily include English texts. This paper describes 20 Spanish-language text corpora—collected to support different tasks related to sentiment analysis, ranging from polarity to emotion categorization. We present a brand-new framework for the characterization of corpora. This includes a number of features to help analyze resources at both corpus level and document level. This survey—besides depicting the overall landscape of corpora in Spanish—supports sentiment analysis practitioners with the task of selecting the most suitable resources.

Arabic is a widely-spoken language with a long and rich history, but existing corpora and language technology focus mostly on modern Arabic and its varieties. Therefore, studying the history of the language has so far been mostly limited to manual analyses on a small scale. In this work, we present a large-scale historical corpus of the written Arabic language, spanning 1400 years. We describe our efforts to clean and process this corpus using Arabic NLP tools, including the identification of reused text. We study the history of the Arabic language using a novel automatic periodization algorithm, as well as other techniques. Our findings confirm the established division of written Arabic into Modern Standard and Classical Arabic, and confirm other established periodizations, while suggesting that written Arabic may be divisible into still further periods of development.

This paper introduces MadSex, a spoken corpus of 54 sociolinguistic interviews in Spanish based on the topic of sexuality. It was collected in order to study the cognitive sociolinguistic variation of sexual concepts. The paper presents and justifies methodological decisions taken during design, collection and transcription stages. Informants were selected in Madrid, based on a pre-stratified sample divided by sex, age and level of education. The interview methodology relied on an opinion questionnaire designed for the indirect elicitation of sexual concepts, which overcame successfully the limitations imposed by the low frequencies of semantic variables in discourse and the impact of sexual taboo in interaction. Relevant aspects of fieldwork, empathy and ethical protocols are also detailed in the paper. Transcription and markup are explained. Finally, an overview of the corpus is given, as well as some research papers based on it. Examples of the questionnaire are also provided.

The paper describes the creation of the first open access multi-genre historical corpus of Emergent Modern Hebrew, made possible by implementation of digital humanities methods in the process of corpus curation, encoding, and dissemination. Corpus contents originate in the Ben-Yehuda Project, an open access repository of Hebrew literature online, and in digital images curated from the collections of the National Library of Israel, a selection of which have been transcribed through a dedicated crowdsourcing task that feeds back into the library’s online catalog. Texts in the corpus are encoded following best practices in the digital humanities, including markup of metadata that enables time-sensitive research, linguistic and other, of the corpus. Evaluation of morphological analysis based on Modern Hebrew language models is shown to distinguish between genres in the historical variety, highlighting the importance of ephemeral materials for linguistic research and for potential collaboration with libraries and cultural institutions in the process of corpus creation. We demonstrate the use of the corpus in diachronic linguistic research and suggest ways in which the association it provides between digital images and texts can be used to support automatic language processing and to enhance resources in the digital humanities.

The latest reference corpus of written Slovene, the Gigafida corpus, was created as part of the ‘Communication in Slovene’ project. In the same project, a web concordancer was designed for the broadest possible use, and tailored to the needs and abilities of user groups such as translators, writers, proofreaders and teachers. Two years after the corpus was published within the new tool, its features were assessed by the users. With an average rate of 4.36 on a scale between 1 and 5 (1 = I strongly disagree, 5 = I strongly agree), the results indicate that most survey participants agreed or strongly agreed with positive statements about the new implementations (e.g. “The corpus results are displayed in a clear manner”). This is a considerable improvement in user experience from the previous reference corpus of Slovene, i.e. the FidaPLUS corpus within the ASP32 concordancer (rated with 3.67). In the user feedback, the simplicity of search options and the interface clarity are highlighted as the main advantages, while for the future development, advanced visualizations of corpus data and improved search of word-phrases are suggested. The evaluation also highlighted some relevant user habits, such as not taking the time to learn systematically about the tool before they start using it. The findings will be implemented in future editions of the Gigafida corpus, but are relevant to any project that aims at facilitating a wider use of reference corpora and corpus-based resources.

We present DEMoS (Database of Elicited Mood in Speech), a new, large database with Italian emotional speech: 68 speakers, some 9 k speech samples. As Italian is under-represented in speech emotion research, for a comparison with the state-of-the-art, we model the ‘big 6 emotions’ and guilt. Besides making available this database for research, our contribution is three-fold: First, we employ a variety of mood induction procedures, whose combinations are especially tailored for specific emotions. Second, we use combinations of selection procedures such as an alexithymia test and self- and external assessment, obtaining 1,5 k (proto-) typical samples; these were used in a perception test (86 native Italian subjects, categorical identification and dimensional rating). Third, machine learning techniques—based on standardised brute-forced openSMILE ComParE features and support vector machine classifiers—were applied to assess how emotional typicality and sample size might impact machine learning efficiency. Our results are three-fold as well: First, we show that appropriate induction techniques ensure the collection of valid samples, whereas the type of self-assessment employed turned out not to be a meaningful measurement. Second, emotional typicality—which shows up in an acoustic analysis of prosodic main features—in contrast to sample size is not an essential feature for successfully training machine learning models. Third, the perceptual findings demonstrate that the confusion patterns mostly relate to cultural rules and to ambiguous emotions.

Definitional knowledge has proved to be essential in various Natural Language Processing tasks and applications, especially when information at the level of word senses is exploited. However, the few sense-annotated corpora of textual definitions available to date are of limited size: this is mainly due to the expensive and time-consuming process of annotating a wide variety of word senses and entity mentions at a reasonably high scale. In this paper we present SenseDefs, a large-scale high-quality corpus of disambiguated definitions (or glosses) in multiple languages, comprising sense annotations of both concepts and named entities from a wide-coverage unified sense inventory. Our approach for the construction and disambiguation of this corpus builds upon the structure of a large multilingual semantic network and a state-of-the-art disambiguation system: first, we gather complementary information of equivalent definitions across different languages to provide context for disambiguation; then we refine the disambiguation output with a distributional approach based on semantic similarity. As a result, we obtain a multilingual corpus of textual definitions featuring over 38 million definitions in 263 languages, and we publicly release it to the research community. We assess the quality of SenseDefs’s sense annotations both intrinsically and extrinsically on Open Information Extraction and Sense Clustering tasks.

This paper describes the ‘Corpus of American Danish’ (CoAmDa), a newly established corpus of spoken immigrant Danish in North and South America. The CoAmDa amounts to approx. 1.7 million tokens, making it one of the largest corpora of heritage language at present. With regard to text type, the CoAmDa is a non-standard multilingual spoken language resource as Danish is mixed with American English, Canadian English or Argentine Spanish, respectively, in every recording. The aim of this note is to document relevant aspects and specifications of the CoAmDA, viz. the audio data, the sociodemographic metadata of the speakers, the digitization process of analog data, the transcription procedures, the format and tagging of the speech files and the internal validation procedures. In so doing, we wish to share our experience and best practices with regard to achieving a spoken language resource of high quality with the interested public, in particular other researchers working on and with multilingual speech corpora.

In this paper we present US2016, the largest publicly available set of corpora of annotated dialogical argumentation. The annotation covers argumentative relations, dialogue acts and pragmatic features. The corpora comprise transcriptions of television debates leading up to the 2016 US presidential elections, and reactions to the debates on Reddit. These two constitutive parts of the corpora are integrated by means of the intertextual correspondence between them. The rhetorical richness and high argument density of the communicative context results in cross-genre corpora that are robust resources for the study of the dialogical dynamics of argumentation in three ways: first, in empirical strands of research in discourse analysis and argumentation studies; second, in the burgeoning field of argument mining where automatic techniques require such data; and third, in formulating algorithmic techniques for sensemaking through the development of Argument Analytics.

TED-Multilingual Discourse Bank, or TED-MDB, is a multilingual resource where TED-talks are annotated at the discourse level in 6 languages (English, Polish, German, Russian, European Portuguese, and Turkish) following the aims and principles of PDTB. We explain the corpus design criteria, which has three main features: the linguistic characteristics of the languages involved, the interactive nature of TED talks—which led us to annotate Hypophora, and the decision to avoid projection. We report our annotation consistency, and post-annotation alignment experiments, and provide a cross-lingual comparison based on corpus statistics.

Swiss dialects of German are, unlike many dialects of other standardised languages, widely used in everyday communication. Despite this fact, automatic processing of Swiss German is still a considerable challenge due to the fact that it is mostly a spoken variety and that it is subject to considerable regional variation. This paper presents the ArchiMob corpus, a freely available general-purpose corpus of spoken Swiss German based on oral history interviews. The corpus is a result of a long design process, intensive manual work and specially adapted computational processing. We first present the modalities of access of the corpus for linguistic, historic and computational research. We then describe how the documents were transcribed, segmented and aligned with the sound source. This work involved a series of experiments that have led to automatically annotated normalisation and part-of-speech tagging layers. Finally, we present several case studies to motivate the use of the corpus for digital humanities in general and for dialectology in particular.

PreMOn is a freely available linguistic resource for exposing predicate models (PropBank, NomBank, VerbNet, and FrameNet) and mappings between them (e.g., SemLink and the predicate matrix) as linguistic linked open data (LOD). It consists of two components: (1) the PreMOn Ontology, that builds on the OntoLex-Lemon model by the W3C ontology-Lexica community group to enable an homogeneous representation of data from various predicate models and their linking to ontological resources; and, (2) the PreMOn Dataset, a LOD dataset integrating various versions of the aforementioned predicate models and mappings, linked to other LOD ontologies and resources (e.g., FrameBase, ESO, WordNet RDF). PreMOn is accessible online in different ways (e.g., SPARQL endpoint), and extensively documented.

SUBTLEX-CAT is a word frequency and contextual diversity database for Catalan, obtained from a 278-million-word corpus based on subtitles supplied from broadcast Catalan television. Like all previous SUBTLEX corpora, it comprises subtitles from films and TV series. In addition, it includes a wider range of TV shows (e.g., news, documentaries, debates, and talk shows) than has been included in most previous databases. Frequency metrics were obtained for the whole corpus, on the one hand, and only for films and fiction TV series, on the other. Two lexical decision experiments revealed that the subtitle-based metrics outperformed the previously available frequency estimates, computed from either written texts or texts from the Internet. Furthermore, the metrics obtained from the whole corpus were better predictors than the ones obtained from films and fiction TV series alone. In both experiments, the best predictor of response times and accuracy was contextual diversity.

This article introduces the second version of the Tool for the Automatic Analysis of Cohesion (TAACO 2.0). Like its predecessor, TAACO 2.0 is a freely available text analysis tool that works on the Windows, Mac, and Linux operating systems; is housed on a user’s hard drive; is easy to use; and allows for batch processing of text files. TAACO 2.0 includes all the original indices reported for TAACO 1.0, but it adds a number of new indices related to local and global cohesion at the semantic level, reported by latent semantic analysis, latent Dirichlet allocation, and word2vec. The tool also includes a source overlap feature, which calculates lexical and semantic overlap between a source and a response text (i.e., cohesion between the two texts based measures of text relatedness). In the first study in this article, we examined the effects that cohesion features, prompt, essay elaboration, and enhanced cohesion had on expert ratings of text coherence, finding that global semantic similarity as reported by word2vec was an important predictor of coherence ratings. A second study was conducted to examine the source and response indices. In this study we examined whether source overlap between the speaking samples found in the TOEFL-iBT integrated speaking tasks and the responses produced by test-takers was predictive of human ratings of speaking proficiency. The results indicated that the percentage of keywords found in both the source and response and the similarity between the source document and the response, as reported by word2vec, were significant predictors of speaking quality. Combined, these findings help validate the new indices reported for TAACO 2.0.

The paper introduces the motivation for creating dedicated speech corpora of air traffic control communication, describes in detail the process of preparation of corpora for both automatic speech recognition and text-to-speech synthesis, presents an illustrative example of speech recognition system developed using the automatic speech recognition corpora and finally describes the technical aspects of the data and the distribution channel.

This article presents the Linguistic Annotated Bibliography (LAB) as a searchable Web portal to quickly and easily access reliable database norms, related programs, and variable calculations. These publications were coded by language, number of stimuli, stimuli type (i.e., words, pictures, symbols), keywords (i.e., frequency, semantics, valence), and other useful information. This tool not only allows researchers to search for the specific type of stimuli needed for experiments but also permits the exploration of publication trends across 100 years of research. Details about the portal creation and use are outlined, as well as various analyses of change in publication rates and keywords. In general, advances in computational power have allowed for the increase in dataset size in the recent decades, in addition to an increase in the number of linguistic variables provided in each publication.

Perceptual experience plays a critical role in the conceptual representation of words. Higher levels of semantic variables such as imageability, concreteness, and sensory experience are generally associated with faster and more accurate word processing. Nevertheless, these variables tend to be assessed mostly on the basis of visual experience. This underestimates the potential contributions of other perceptual modalities. Accordingly, recent evidence has stressed the importance of providing modality-specific perceptual strength norms. In the present study, we developed French Canadian norms of visual and auditory perceptual strength (i.e., the modalities that have major impact on word processing) for 3,596 nouns. We then explored the relationship between these newly developed variables and other lexical, orthographic, and semantic variables. Finally, we demonstrated the contributions of visual and auditory perceptual strength ratings to visual word processing beyond those of other semantic variables related to perceptual experience (e.g., concreteness, imageability, and sensory experience ratings). The ratings developed in this study are a meaningful contribution toward the implementation of new studies that will shed further light on the interaction between linguistic, semantic, and perceptual systems.

In the last decade, research has shown that word processing is influenced by the lexical and semantic features of words. However, norms for a crucial semantic variable—that is, conceptual familiarity—have not been available for a sizeable French database. We thus developed French Canadian conceptual familiarity norms for 3,596 nouns. This enriches Desrochers and Thompson’s (2009) database, in which subjective frequency and imageability values are already available for the same words. We collected online data from 313 Canadian French speakers. The full database of conceptual familiarity ratings is freely available at http://lingualab.ca/fr/projets/normes-de-familiarite-conceptuelle. We then demonstrated the utility of these new conceptual familiarity norms by assessing their contribution to lexical decision times. We conducted a stepwise regression model with conceptual familiarity in the last step. This allowed us to assess the independent contribution of conceptual familiarity beyond the contributions of other well-known psycholinguistic variables, such as frequency, imageability, and age of acquisition. The results showed that conceptual familiarity facilitated lexical decision latencies. In sum, these ratings will help researchers select French stimuli for experiments in which conceptual familiarity must be taken into account.

A limiting factor in understanding memory and language is often the availability of large numbers of stimuli to use and explore in experimental studies. In this study, we expand on three previous databases of concepts to over 4000 words including nouns, verbs, adjectives, and other parts of speech. Participants in the study were asked to provide lists of features for each concept presented (a semantic feature production task), which were combined with previous research in this area. These feature lists for each concept were then coded into their root word form and affixes (i.e., cat and s for cats) to explore the impact of word form on semantic similarity measures, which are often calculated by comparing concept feature lists (feature overlap). All concept features, coding, and calculated similarity information is provided in a searchable database for easy access and utilization for future researchers when designing experiments that use word stimuli. The final database of word pairs was combined with the Semantic Priming Project to examine the relation of semantic similarity statistics on semantic priming in tandem with other psycholinguistic variables.

Paired-associate learning is one of the most commonly used paradigms to study human memory. In many of these studies, participants are typically told to learn foreign language–English translations, such as Swahili–English or Lithuanian–English pairs. One limitation of these currently available foreign language–English translation norms is that their foreign languages are based on the alphabetic writing system, thereby preventing researchers from generalizing their findings to languages based on logographic writing systems. In the present study we collected normative data for 160 Chinese–English word pairs. Participants completed three study–test cycles, followed by metacognitive judgments on their learning experience. For each pair, we report recall performance, recall latency, ease of learning, and judgments of learning. A simultaneous multiple regression analysis with frequency (of both the English word and the Chinese character), word length (English), and number of strokes (Chinese) as predictors revealed that a greater number of strokes (or higher visual complexity) for the Chinese characters predicted lower target recall.

An unprecedented number of empirical studies have shown that iconic gestures—those that mimic the sensorimotor attributes of a referent—contribute significantly to language acquisition, perception, and processing. However, there has been a lack of normed studies describing generalizable principles in gesture production and in comprehension of the mappings of different types of iconic strategies (i.e., modes of representation; Müller, 2013). In Study 1 we elicited silent gestures in order to explore the implementation of different types of iconic representation (i.e., acting, representing, drawing, and personification) to express concepts across five semantic domains. In Study 2 we investigated the degree of meaning transparency (i.e., iconicity ratings) of the gestures elicited in Study 1. We found systematicity in the gestural forms of 109 concepts across all participants, with different types of iconicity aligning with specific semantic domains: Acting was favored for actions and manipulable objects, drawing for nonmanipulable objects, and personification for animate entities. Interpretation of gesture–meaning transparency was modulated by the interaction between mode of representation and semantic domain, with some couplings being more transparent than others: Acting yielded higher ratings for actions, representing for object-related concepts, personification for animate entities, and drawing for nonmanipulable entities. This study provides mapping principles that may extend to all forms of manual communication (gesture and sign). This database includes a list of the most systematic silent gestures in the group of participants, a notation of the form of each gesture based on four features (hand configuration, orientation, placement, and movement), each gesture’s mode of representation, iconicity ratings, and professionally filmed videos that can be used for experimental and clinical endeavors.

Most of the new words a reader will find are morphologically complex. Also, theoretical models of language processing propose that morphology plays an important role in visual word processing. Nevertheless, studies on the subject show contradicting results that are difficult to reconcile. One factor that may explain this is the lack of a sizeable and reliable morphological database. As a consequence, there are enormous methodological differences in the way the values for morphological variables are calculated across studies. We present a sizeable and freely available database with six new variables for affixes and three for roots for 68,624 words from the English Lexicon Project. We further studied by means of regression models the influence of these new variables on the lexical decision latencies of 4,724 morphologically complex nouns that included one root and one suffix. Results showed that root frequency and suffix length had a facilitatory effect, whereas the percentage of more frequent words in the morphological family of the suffix had an inhibitory effect on latencies. After controlling for collinearity, root family size, suffix family size, suffix P*, and suffix frequency also had facilitatory effects. These results shed new light on the importance of suffix length and the frequency of the lexical competitors of the family of a suffix. This database represents a valuable resource for studies on the effect of morphology in visual word processing in English and can be found at https://github.com/hugomailhot/MorphoLex-en .

The Lesser Sunda Islands in eastern Indonesia cover a longitudinal distance of some 600 kilometres. They are the westernmost place where languages of the Austronesian family come into contact with a family of Papuan languages and constitute an area of high linguistic diversity. Despite its diversity, the Lesser Sundas are little studied and for most of the region, written historical records, as well as archaeological and ethnographic data are lacking. In such circumstances the study of relationships between languages through their lexicon is a unique tool for making inferences about human (pre-)history and tracing population movements. However, the lack of a collective body of lexical data has severely limited our understanding of the history of the languages and peoples in the Lesser Sundas. The LexiRumah database fills this gap by assembling lexicons of Lesser Sunda languages from published and unpublished sources, and making those lexicons available online in a consistent format. T)

Words that correspond to a potential sensory experience-concrete words-have long been found to possess a processing advantage over abstract words in various lexical tasks. We collected norms of concreteness for a set of 1,659 French words, together with other psycholinguistic norms that were not available for these words-context availability, emotional valence, and arousal-but which are important if we are to achieve a better understanding of the meaning of concreteness effects. We then investigated the relationships of concreteness with these newly collected variables, together with other psycholinguistic variables that were already available for this set of words (e.g., imageability, age of acquisition, and sensory experience ratings). Finally, thanks to the variety of psychological norms available for this set of words, we decided to test further the embodied account of concreteness effects in visual-word recognition, championed by Kousta, Vigliocco, Vinson, Andrews, and Del Campo (Journal of Experimental Psychology: General, 140, 14-34, 2011). Similarly, we investigated the influences of concreteness in three word recognition tasks-lexical decision, progressive demasking, and word naming-using a multiple regression approach, based on the reaction times available in Chronolex (Ferrand, Brysbaert, Keuleers, New, Bonin, M{\'{e}}ot, Pallier, Frontiers in Psychology, 2; 306, 2011). The norms can be downloaded as supplementary material provided with this article.

This paper presents the different methodologies and resources used to build Galnet, the Galician version of WordNet. It reviews the different extraction processes and the lexicographical and textual sources used to develop this resource, and describes some of its applications in ontology research and terminology processing.

We present the Chinese Lexical Database (CLD): a large-scale lexical database for simplified Chinese. The CLD provides a wealth of lexical information for 3913 one-character words, 34,233 two-character words, 7143 three-character words, and 3355 four-character words, and is publicly available through http://www.chineselexicaldatabase.com. For each of the 48,644 words in the CLD, we provide a wide range of categorical predictors, as well as an extensive set of frequency measures, complexity measures, neighborhood density measures, orthography-phonology consistency measures, and information-theoretic measures. We evaluate the explanatory power of the lexical variables in the CLD in the context of experimental data through analyses of lexical decision latencies for one-character, two-character, three-character and four-character words, as well as word naming latencies for one-character and two-character words. The results of these analyses are discussed.

Sensory experience rating (SER) is a recently developed subjective lexical index that reflects the extent to which a word evokes a sensory and/or perceptual experience in a reader (Juhasz {\&} Yap, 2013; Juhasz, Yap, Dicke, Taylor, {\&} Gullick, 2011). In the present study, SERs for a set of 5,500 Spanish words were collected, which makes this the largest set of norms for SER in the Spanish language to date. Additionally, with the aim of further exploring the implications of this new indicator and its relations with other psycholinguistic variables, a variety of correlational and regression analyses are provided. The results showed that SERs significantly correlated with imageability, age of acquisition, and a number of variables related to perception and emotion. In addition, SERs predicted a significant amount of variance in lexical decision times when other variables were controlled.

VerbNet—the most extensive online verb lexicon currently available for English—has proved useful in supporting a variety of NLP tasks. However, its exploitation in multilingual NLP has been limited by the fact that such classifications are available for few languages only. Since manual development of VerbNet is a major undertaking, researchers have recently translated VerbNet classes from English to other languages. However, no systematic investigation has been conducted into the applicability and accuracy of such a translation approach across different, typologically diverse languages. Our study is aimed at filling this gap. We develop a systematic method for translation of VerbNet classes from English to other languages which we first apply to Polish and subsequently to Croatian, Mandarin, Japanese, Italian, and Finnish. Our results on Polish demonstrate high translatability with all the classes (96% of English member verbs successfully translated into Polish) and strong inter-annotator agreement, revealing a promising degree of overlap in the resultant classifications. The results on other languages are equally promising. This demonstrates that VerbNet classes have strong cross-lingual potential and the proposed method could be applied to obtain gold standards for automatic verb classification in different languages. We make our annotation guidelines and the six language-specific verb classifications available with this paper.

Iconicity – the correspondence between form and meaning – may help young children learn to use new words. Early‐learned words are higher in iconicity than later learned words. However, it remains unclear what role iconicity may play in actual language use. Here, we ask whether iconicity relates not just to the age at which words are acquired, but also to how frequently children and adults use the words in their speech. If iconicity serves to bootstrap word learning, then we would expect that children should say highly iconic words more frequently than less iconic words, especially early in development. We would also expect adults to use iconic words more often when speaking to children than to other adults. We examined the relationship between frequency and iconicity for approximately 2000 English words. Replicating previous findings, we found that more iconic words are learned earlier. Moreover, we found that more iconic words tend to be used more by younger children, and adults use more iconic words when speaking to children than to other adults. Together, our results show that young children not only learn words rated high in iconicity earlier than words low in iconicity, but they also produce these words more frequently in conversation – a pattern that is reciprocated by adults when speaking with children. Thus, the earliest conversations of children are relatively higher in iconicity, suggesting that this iconicity scaffolds the production and comprehension of spoken language during early development.

The paper introduces a novel annotated corpus of Old and Middle Hungarian (16–18 century), the texts of which were selected in order to approximate the vernacular of the given historical periods as closely as possible. The corpus consists of testimonies of witnesses in trials and samples of private correspondence. The texts are not only analyzed morphologically, but each file contains metadata that would also facilitate sociolinguistic research. The texts were segmented into clauses, manually normalized and morphosyntactically annotated using an annotation system consisting of the PurePos PoS tagger and the Hungarian morphological analyzer HuMor originally developed for Modern Hungarian but adapted to analyze Old and Middle Hungarian morphological constructions. The automatically disambiguated morphological annotation was manually checked and corrected using an easy-to-use web-based manual disambiguation interface. The normalization process and the manual validation of the annotation required extensive teamwork and provided continuous feedback for the refinement of the computational morphology and iterative retraining of the statistical models of the tagger. The paper discusses some of the typical problems that occurred during the normalization procedure and their tentative solutions. Besides, we also describe the automatic annotation tools, the process of semi-automatic disambiguation, and the query interface, a special function of which also makes correction of the annotation possible. Displaying the original, the normalized and the parsed versions of the selected texts, the beta version of the first fully normalized and annotated historical corpus of Hungarian is freely accessible at the address http://tmk.nytud.hu/.

In this work we present the Talk of Norway (ToN) data set, a collection of Norwegian Parliament speeches from 1998 to 2016. Every speech is richly annotated with metadata harvested from different sources, and augmented with language type, sentence, token, lemma, part-of-speech, and morphological feature annotations. We also present a pilot study on party classification in the Norwegian Parliament, carried out in the context of a cross-faculty collaboration involving researchers from both Political Science and Computer Science. Our initial experiments demonstrate how the linguistic and institutional annotations in ToN can be used to gather insights on how different aspects of the political process affect classification.

We present the RST Signalling Corpus (Das et al. in RST signalling corpus, LDC2015T10. https://catalog.ldc.upenn.edu/LDC2015T10, 2015), a corpus annotated for signals of coherence relations. The corpus is developed over the RST Discourse Treebank (Carlson et al. in RST Discourse Treebank, LDC2002T07. https://catalog.ldc.upenn.edu/LDC2002T07, 2002) which is annotated for coherence relations. In the RST Signalling Corpus, these relations are further annotated with signalling information. The corpus includes annotation not only for discourse markers which are considered to be the most typical (or sometimes the only type of) signals in discourse, but also for a wide array of other signals such as reference, lexical, semantic, syntactic, graphical and genre features as potential indicators of coherence relations. We describe the research underlying the development of the corpus and the annotation process, and provide details of the corpus. We also present the results of an inter-annotator agreement study, illustrating the validity and reproducibility of the annotation. The corpus is available through the Linguistic Data Consortium, and can be used to investigate the psycholinguistic mechanisms behind the interpretation of relations through signalling, and also to develop discourse-specific computational systems such as discourse parsing applications.

The use of immersive virtual reality as a research tool is rapidly increasing in numerous scientific disciplines. By combining ecological validity with strict experimental control, immersive virtual reality provides the potential to develop and test scientific theories in rich environments that closely resemble everyday settings. This article introduces the first standardized database of colored three-dimensional (3-D) objects that can be used in virtual reality and augmented reality research and applications. The 147 objects have been normed for name agreement, image agreement, familiarity, visual complexity, and corresponding lexical characteristics of the modal object names. The availability of standardized 3-D objects for virtual reality research is important, because reaching valid theoretical conclusions hinges critically on the use of well-controlled experimental stimuli. Sharing standardized 3-D objects across different virtual reality labs will allow for science to move forward more quickly.

Quality annotated resources are essential for Natural Language Processing. The objective of this work is to present a corpus of clinical narratives in French annotated for linguistic, semantic and structural information, aimed at clinical information extraction. Six annotators contributed to the corpus annotation, using a comprehensive annotation scheme covering 21 entities, 11 attributes and 37 relations. All annotators trained on a small, common portion of the corpus before proceeding independently. An automatic tool was used to produce entity and attribute pre-annotations. About a tenth of the corpus was doubly annotated and annotation differences were resolved in consensus meetings. To ensure annotation consistency throughout the corpus, we devised harmonization tools to automatically identify annotation differences to be addressed to improve the overall corpus quality. The annotation project spanned over 24 months and resulted in a corpus comprising 500 documents (148,476 tokens) annotated with 44,740 entities and 26,478 relations. The average inter-annotator agreement is 0.793 F-measure for entities and 0.789 for relations. The performance of the pre-annotation tool for entities reached 0.814 F-measure when sufficient training data was available. The performance of our entity pre-annotation tool shows the value of the corpus to build and evaluate information extraction methods. In addition, we introduced harmonization methods that further improved the quality of annotations in the corpus.

This article introduces a new corpus of eye movements in silent reading—the Russian Sentence Corpus (RSC). Russian uses the Cyrillic script, which has not yet been investigated in cross-linguistic eye movement research. As in every language studied so far, we confirmed the expected effects of low-level parameters, such as word length, frequency, and predictability, on the eye movements of skilled Russian readers. These findings allow us to add Slavic languages using Cyrillic script (exemplified by Russian) to the growing number of languages with different orthographies, ranging from the Roman-based European languages to logographic Asian ones, whose basic eye movement benchmarks conform to the universal comparative science of reading (Share, 2008). We additionally report basic descriptive corpus statistics and three exploratory investigations of the effects of Russian morphology on the basic eye movement measures, which illustrate the kinds of questions that researchers can answer using the RSC. The annotated corpus is freely available from its project page at the Open Science Framework: https://osf.io/x5q2r/.

In this paper we present the corpus of Basque simplified texts. This corpus compiles 227 original sentences of science popularisation domain and two simplified versions of each sentence. The simplified versions have been created following different approaches: the structural, by a court translator who considers easy-to-read guidelines and the intuitive, by a teacher based on her experience. The aim of this corpus is to make a comparative analysis of simplified text. To that end, we also present the annotation scheme we have created to annotate the corpus. The annotation scheme is divided into eight macro-operations: delete, merge, split, transformation, insert, reordering, no operation and other. These macro-operations can be classified into different operations. We also relate our work and results to other languages. This corpus will be used to corroborate the decisions taken and to improve the design of the automatic text simplification system for Basque.

In this paper, we present SFU ReviewSP-NEG, the first Spanish corpus annotated with negation with a wide coverage freely available. We describe the methodology applied in the annotation of the corpus including the tagset, the linguistic criteria and the inter-annotator agreement tests. We also include a complete typology of negation patterns in Spanish. This typology has the advantage that it is easy to express in terms of a tagset for corpus annotation: the types are clearly defined, which avoids ambiguity in the annotation process, and they provide wide coverage (i.e. they resolved all the cases occurring in the corpus). We use the SFU ReviewSP as a base in order to make the annotations. The corpus consists of 400 reviews, 221,866 words and 9455 sentences, out of which 3022 sentences contain at least one negation structure.

In this study, we report the validation results of the EU-Emotion Voice Database, an emotional voice database available for scientific use, containing a total of 2,159 validated emotional voice stimuli. The EU-Emotion voice stimuli consist of audio-recordings of 54 actors, each uttering sentences with the intention of conveying 20 different emotional states (plus neutral). The database is organized in three separate emotional voice stimulus sets in three different languages (British English, Swedish, and Hebrew). These three sets were independently validated by large pools of participants in the UK, Sweden, and Israel. Participants' validation of the stimuli included emotion categorization accuracy and ratings of emotional valence, intensity, and arousal. Here we report the validation results for the emotional voice stimuli from each site and provide validation data to download as a supplement, so as to make these data available to the scientific community. The EU-Emotion Voice Database is part of the EU-Emotion Stimulus Set, which in addition contains stimuli of emotions expressed in the visual modality (by facial expression, body language, and social scene) and is freely available to use for academic research purposes.

Geographical data can be obtained by converting place names from free-format text into geographical coordinates. The ability to geo-locate events in textual reports represents a valuable source of information in many real-world applications such as emergency responses, real-time social media geographical event analysis, understanding location instructions in auto-response systems and more. However, geoparsing is still widely regarded as a challenge because of domain language diversity, place name ambiguity, metonymic language and limited leveraging of context as we show in our analysis. Results to date, whilst promising, are on laboratory data and unlike in wider NLP are often not cross-compared. In this study, we evaluate and analyse the performance of a number of leading geoparsers on a number of corpora and highlight the challenges in detail. We also publish an automatically geotagged Wikipedia corpus to alleviate the dearth of (open source) corpora in this domain.

This paper presents the Irish Political Speech Database, an English-language database collected from Irish political recordings. The database is collected with automated indexing and content retrieval in mind, and thus is gathered from real-world recordings (such as television interviews and election rallies) which represent the nature and quality of recordings which will be encountered in practical applications. The database is labelled for six speaker attributes: boring; charismatic; enthusiastic; inspiring; likeable; and persuasive. Each of these traits is linked to the perceived ability or appeal of the speaker, and as such are relevant to a range of content retrieval and speech analysis tasks. The six base attributes are combined to form a metric of Overall Speaker Appeal. A set of baseline experiments is presented, which demonstrate the potential of this database for affective computing studies. Classification accuracies of up to 76% are achieved, with little feature or system optimisation.

A central issue in visual and spoken word recognition is the lexical representation of complex words-in particular, whether the lexical representation of complex words depends on semantic transparency: Is a complex verb like understand lexically represented as a whole word or via its base stand, given that its meaning is not transparent from the meanings of its parts? To study this issue, a number of stimulus characteristics are of interest that are not yet available in public databases of German. This article provides semantic association ratings, lexical paraphrases, and vector-based similarity measures for German verbs, measuring (a) the semantic transparency between 1,259 complex verbs and their bases, (b) the semantic relatedness between 1,109 verb pairs with 432 different bases, and (c) the vector-based similarity measures of 846 verb pairs. Additionally, we include the verb regularity of all verbs and two counts of verb family size for 184 base verbs, as well as estimates of age of acquisition and age of reading for 200 verbs. Together with lemma and type frequencies from public lexical databases, all measures can be downloaded along with this article. Statistical analyses indicate that verb family size, morphological complexity, frequency, and verb regularity affect the semantic transparency and relatedness ratings as well as the age of acquisition estimates, indicating that these are relevant variables in psycholinguistic experiments. Although lexical paraphrases, vector-based similarity measures, and semantic association ratings may deliver complementary information, the interrater reliability of the semantic association ratings for each verb pair provides valuable information when selecting stimuli for psycholinguistic experiments.

In this article, we present Procura-PALavras (P-PAL), a Web-based interface for a new European Portuguese (EP) lexical database. Based on a contemporary printed corpus of over 227 million words, P-PAL provides a broad range of word attributes and statistics, including several measures of word frequency (e.g., raw counts, per-million word frequency, logarithmic Zipf scale), morpho-syntactic information (e.g., parts of speech [PoSs], grammatical gender and number, dominant PoS, and frequency and relative frequency of the dominant PoS), as well as several lexical and sublexical orthographic (e.g., number of letters; consonant-vowel orthographic structure; density and frequency of orthographic neighbors; orthographic Levenshtein distance; orthographic uniqueness point; orthographic syllabification; and trigram, bigram, and letter type and token frequencies), and phonological measures (e.g., pronunciation, number of phonemes, stress, density and frequency of phonological neighbors, transposed and phonographic neighbors, syllabification, and biphone and phone type and token frequencies) for {\~{}}53,000 lemmatized and {\~{}}208,000 nonlemmatized EP word forms. To obtain these metrics, researchers can choose between two word queries in the application: (i) analyze words previously selected for specific attributes and/or lexical and sublexical characteristics, or (ii) generate word lists that meet word requirements defined by the user in the menu of analyses. For the measures it provides and the flexibility it allows, P-PAL will be a key resource to support research in all cognitive areas that use EP verbal stimuli. P-PAL is freely available at http://p-pal.di.uminho.pt/tools .

Human locomotion is a fundamental class of events, and manners of locomotion (e.g., how the limbs are used to achieve a change of location) are commonly encoded in language and gesture. To our knowledge, there is no openly accessible database containing normed human locomotion stimuli. Therefore, we introduce the GestuRe and ACtion Exemplar (GRACE) video database, which contains 676 videos of actors performing novel manners of human locomotion (i.e., moving from one location to another in an unusual manner) and videos of a female actor producing iconic gestures that represent these actions. The usefulness of the database was demonstrated across four norming experiments. First, our database contains clear matches and mismatches between iconic gesture videos and action videos. Second, the male actors and female actors whose action videos matched the gestures in the best possible way, perform the same actions in very similar manners and different actions in highly distinct manners. Third, all the actions in the database are distinct from each other. Fourth, adult native English speakers were unable to describe the 26 different actions concisely, indicating that the actions are unusual. This normed stimuli set is useful for experimental psychologists working in the language, gesture, visual perception, categorization, memory, and other related domains.

The adoption of electronic health records (EHRs) has enabled a wide range of applications leveraging EHR data. However, the meaningful use of EHR data largely depends on our ability to efficiently extract and consolidate information embedded in clinical text where natural language processing (NLP) techniques are essential. Semantic textual similarity (STS) that measures the semantic similarity between text snippets plays a significant role in many NLP applications. In the general NLP domain, STS shared tasks have made available a huge collection of text snippet pairs with manual annotations in various domains. In the clinical domain, STS can enable us to detect and eliminate redundant information that may lead to a reduction in cognitive burden and an improvement in the clinical decision-making process. This paper elaborates our efforts to assemble a resource for STS in the medical domain, MedSTS. It consists of a total of 174,629 sentence pairs gathered from a clinical corpus at Mayo Clinic. A subset of MedSTS (MedSTS_ann) containing 1068 sentence pairs was annotated by two medical experts with semantic similarity scores of 0–5 (low to high similarity). We further analyzed the medical concepts in the MedSTS corpus, and tested four STS systems on the MedSTS_ann corpus. In the future, we will organize a shared task by releasing the MedSTS_ann corpus to motivate the community to tackle the real world clinical problems.

Presents a study which aims to investigate SPALEX, a Spanish lexical decision database by focusing on native Spanish speakers at a global scale and with a vast amount of words, to provide a useful tool for researchers exploring the acquisition and processing of this language in native and foreign contexts. SPALEX contains data from a Spanish crowd-sourced lexical decision mega study. The authors collected the data through an online platform from May 12th, 2014 to December 19th, 2017. The majority of the data was acquired during the first month of the experiment, when an advertising campaign was done in order to attract the public’s attention. Participants also had the option of publishing their results via social networks, which led to attract more participants in a snow-ball sampling fashion. Additionally, the database contains information on participants that voluntarily provided information about their gender, age, country of origin, education level, handedness, native language, and best foreign language. In each experimental session, participants responded to 70 words and 30 non-words presented randomly and without repetition. Accuracy in SPALEX is expressed as 1 for correct answers and 0 for incorrect answers. Based on participants’ responses, the authors calculated percentage known, a measure of the percentage of participants that know a particular word. (PsycINFO Database Record (c) 2018 APA, all rights reserved)

Sentiment lexicons and word embeddings constitute well-established sources of information for sentiment analysis in online social media. Although their effectiveness has been demonstrated in state-of-the-art sentiment analysis and related tasks in the English language, such publicly available resources are much less developed and evaluated for the Greek language. In this paper, we tackle the problems arising when analyzing text in such an under-resourced language. We present and make publicly available a rich set of such resources, ranging from a manually annotated lexicon, to semi-supervised word embedding vectors and annotated datasets for different tasks. Our experiments using different algorithms and parameters on our resources show promising results over standard baselines; on average, we achieve a 24.9% relative improvement in F-score on the cross-domain sentiment analysis task when training the same algorithms with our resources, compared to training them on more traditional feature sources, such as n-grams. Importantly, while our resources were built with the primary focus on the cross-domain sentiment analysis task, they also show promising results in related tasks, such as emotion analysis and sarcasm detection.

Feature stability, time and tempo of change, and the role of genealogy versus areality in creating linguistic diversity are important issues in current computational research on linguistic typology. This paper presents a database initiative, DiACL Typology, which aims to provide a resource for addressing these questions with specific of the extended Indo-European language area of Eurasia, the region with the best documented linguistic history. The database is pre-prepared for statistical and phylogenetic analyses and contains both linguistic typological data from languages spanning over four millennia, and linguistic metadata concerning geographic location, time period, and reliability of sources. The typological data has been organized according to a hierarchical model of increasing granularity in order to create datasets that are complete and representative. [ABSTRACT FROM AUTHOR], Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied o)

SNOMED CT provides about 300,000 codes with fine-grained concept definitions to support interoperability of health data. Coding clinical texts with medical terminologies it is not a trivial task and is prone to disagreements between coders. We conducted a qualitative analysis to identify sources of disagreements on an annotation experiment which used a subset of SNOMED CT with some restrictions. A corpus of 20 English clinical text fragments from diverse origins and languages was annotated independently by two domain medically trained annotators following a specific annotation guideline. By following this guideline, the annotators had to assign sets of SNOMED CT codes to noun phrases, together with concept and term coverage ratings. Then, the annotations were manually examined against a reference standard to determine sources of disagreements. Five categories were identified. In our results, the most frequent cause of inter-annotator disagreement was related to human issues. In severa)

Normative databases containing psycholinguistic variables are commonly used to aid stimulus selection for investigations into language and other cognitive processes. Norms exist for many languages, but not for Thai. The aim of the present research, therefore, was to obtain Thai normative data for the BOSS, a set of 480 high resolution color photographic images of real objects (Brodeur et al. in PLoS ONE 5(5), 2010. https://doi.org/10.1371/journal.pone.0010773 ). Norms were provided by 584 Thai university students on eight dimensions: name agreement, object familiarity, visual complexity, category agreement, image agreement, two types of manipulability (graspability and mimeability), and age of acquisition. The results revealed comparatively similar levels of name agreement to Brodeur et al. especially when unfamiliar items were factored out. The pattern of intercorrelations among the Thai psycholinguistic norms was comparable to previous studies and our cross-linguistic correlations were robust for the same set of pictures in English and French. Conjointly, the findings extend the relevancy of the BOSS to Thailand, supporting this photographic resource for investigations of language and other cognitive processes in monolingual, multilingual, and brain-impaired populations.

A major obstacle for the design of rigorous, reproducible studies in moral psychology is the lack of suitable stimulus sets. Here, we present the Socio-Moral Image Database (SMID), the largest standardized moral stimulus set assembled to date, containing 2,941 freely available photographic images, representing a wide range of morally (and affectively) positive, negative and neutral content. The SMID was validated with over 820,525 individual judgments from 2,716 participants, with normative ratings currently available for all images on affective valence and arousal, moral wrongness, and relevance to each of the five moral values posited by Moral Foundations Theory. We present a thorough analysis of the SMID regarding (1) inter-rater consensus, (2) rating precision, and (3) breadth and variability of moral content. Additionally, we provide recommendations for use aimed at efficient study design and reproducibility, and outline planned extensions to the database. We anticipate that the SMID will serve as a useful resource for psychological, neuroscientific and computational (e.g., natural language processing or computer vision) investigations of social, moral and affective processes. The SMID images, along with associated normative data and additional resources are available at https://osf.io/2rqad/.

In this work, we report large-scale semantic role annotation of arguments in the Turkish dependency treebank, and present the first comprehensive Turkish semantic role labeling (SRL) resource: Turkish Proposition Bank (PropBank). We present our annotation workflow that harnesses crowd intelligence, and discuss the procedures for ensuring annotation consistency and quality control. Our discussion focuses on syntactic variations in realization of predicate-argument structures, and the large lexicon problem caused by complex derivational morphology. We describe our approach that exploits framesets of root verbs to abstract away from syntax and increase self-consistency of the Turkish PropBank. The issues that arise in the annotation of verbs derived via valency changing morphemes, verbal nominals, and nominal verbs are explored, and evaluation results for inter-annotator agreement are provided. Furthermore, semantic layer described here is aligned with universal dependency (UD) compliant treebank and released to enable more researchers to work on the problem. Finally, we use PropBank to establish a baseline score of 79.10 F1 for Turkish SRL using the mate-tool (an open-source SRL tool based on supervised machine learning) enhanced with basic morphological features. Turkish PropBank and the extended SRL system are made publicly available.

Treebanks are important resources for researchers in natural language processing. They provide training and testing materials so that different algorithms can be compared. However, it is not a trivial task to construct high-quality treebanks. We have not yet had a proper treebank for such a low-resource language as Vietnamese, which has probably lowered the performance of Vietnamese language processing. We have been building a consistent and accurate Vietnamese treebank to alleviate such situations. Our treebank is annotated with three layers: word segmentation, part-of-speech tagging, and bracketing. We developed detailed annotation guidelines for each layer by presenting Vietnamese linguistic issues as well as methods of addressing them. Here, we also describe approaches to controlling annotation quality while ensuring a reasonable annotation speed. We specifically designed an appropriate annotation process and an effective process to train annotators. In addition, we implemented several support tools to improve annotation speed and to control the consistency of the treebank. The results from experiments revealed that both inter-annotator agreement and accuracy were higher than 90%, which indicated that the treebank is reliable.

In this paper we introduce the Kurdish BLARK (Basic Language Resource Kit). The original BLARK has not considered multi-dialect characteristics and generally has targeted reasonably well-resourced languages. To consider these two features, we extended BLARK and applied the proposed extension to Kurdish. Kurdish language not only faces a paucity in resources, but also embraces several dialects within a complex linguistic context. This paper presents the Kurdish BLARK and shows that from Natural language processing and computational linguistics perspectives the revised BLARK provides a more applicable view of languages with similar characteristics to Kurdish.

Humans make moral judgments every day, and research demonstrates that these evaluations are based on a host of related event features (e.g., harm, legality). In order to acquire systematic data on how moral judgments are made, our assessments need to be expanded to include real-life, ecologically valid stimuli that take into account the numerous event features that are known to influence moral judgment. To facilitate this, Knutson et al. (in Social Cognitive and Affective Neuroscience, 5(4), 378–384, 2010) developed vignettes based on real-life episodic memories rated concurrently on key moral features; however, the method is time intensive (~1.4–3.4 h) and the stimuli and ratings require further validation and characterization. The present study addresses these limitations by: (i) validating three short subsets of these vignettes (39 per subset) that are time-efficient (10–25 min per subset) yet representative of the ratings and factor structure of the full set, (ii) norming ratings of moral features in a larger sample (total N = 661, each subset N = ~220 vs. Knutson et al. N = 30), (iii) examining the generalizability of the original factor structure by replicating it in a larger sample across vignette subsets, sex, and political ideology, and (iv) using latent profile analysis to empirically characterize vignette groupings based on event feature ratings profiles and vignette content. This study therefore provides researchers with a core battery of well-characterized and realistic vignettes, concurrently rated on key moral features that can be administered in a brief, time-efficient manner to advance research on the nature of moral judgment.

Numerous studies in psychology, cognitive neuroscience and psycholinguistics have used pictures of objects as stimulus materials. Currently, authors engaged in cross-linguistic work or wishing to run parallel studies at multiple sites where different languages are spoken must rely on rather small sets of black-and-white or colored line drawings. These sets are increasingly experienced as being too limited. Therefore, we constructed a new set of 750 colored pictures of concrete concepts. This set, MultiPic, constitutes a new valuable tool for cognitive scientists investigating language, visual perception, memory and/or attention in monolingual or multilingual populations. Importantly, the MultiPic databank has been normed in six different European languages (British English, Spanish, French, Dutch, Italian and German). All stimuli and norms are freely available at http://www.bcbl.eu/databases/multipic.

Recent years have witnessed a growing interest in behavioral and neuroimaging studies on the processing of symbolic communicative gestures, such as pantomimes and emblems, but well-controlled stimuli have been scarce. This study describes a dataset of more than 200 video clips of an actress performing pantomimes (gestures that mimic object-directed/object-use actions; e.g., playing guitar), emblems (conventional gestures; e.g., thumbs up), and meaningless gestures. Gestures were divided into four lists. For each of these four lists, 50 Italian and 50 American raters judged the meaningfulness of the gestures and provided names and descriptions for them. The results of these rating and norming measures are reported separately for the Italian and American raters, offering the first normed set of meaningful and meaningless gestures for experimental studies. The stimuli are available for download via the Figshare database.

The Massive Auditory Lexical Decision (MALD) database is an end-to-end, freely available auditory and production data set for speech and psycholinguistic research, providing time-aligned stimulus recordings for 26,793 words and 9592 pseudowords, and response data for 227,179 auditory lexical decisions from 231 unique monolingual English listeners. In addition to the experimental data, we provide many precompiled listener- and item-level descriptor variables. This data set makes it easy to explore responses, build and test theories, and compare a wide range of models. We present summary statistics and analyses.

We report normative data from a large (N = 307) sample of young adult participants tested with a computerized version of the long form of the classical Benton Facial Recognition Test (BFRT; Benton {\&} Van Allen, 1968). The BFRT-c requires participants to match a target face photograph to either one or three of six face photographs presented simultaneously. We found that the percent accuracy on the BFRT-c (81{\%}-83{\%}) was below ceiling yet well above chance level, with little interindividual variance in this typical population sample, two important aspects of a sensitive clinical test. Although the split-half reliability on response accuracy was relatively low, due to the large variability in difficulty across items, the correct response times measured in this version-completed in 3 min, on average-provide a reliable and critical complementary measure of performance at individual unfamiliar-face matching. In line with previous observations from other measures, females outperformed male participants at the BFRT-c, especially for female faces. In general, performance was also lower following lighting changes than following head rotations, in line with previous studies that have emphasized participants' limited ability to match pictures of unfamiliar faces with important variations in illumination. Overall, this normative data set supports the validity of the BFRT-c as a key component of a battery of tests to identify clinical impairments in individual face recognition, such as observed in acquired prosopagnosia. However, this analysis strongly recommends that researchers consider the full test results: Beyond global indexes of performance based on accuracy rates only, they should consider the time taken to match individual faces as well as the variability in performance across items.

Subjective ratings of perceptual and motor attributes were obtained for a set of 750 concrete concepts in Spanish by requiring scale-based judgments from a sample of university students (N = 539). Following on the work of Amsel, Urbach, and Kutas (2012), the seven attributes were color, motion, sound, smell, taste, graspability, and pain. Normative data based on the obtained ratings are provided as a tool for future investigations. Additionally, the relationships of these attributes to other lexical dimensions (e.g., familiarity, frequency, concreteness) and the factorial organization of concepts around the main components were analyzed. The pattern of results is consistent with prior findings that highlight the relevance of dimensions related to survival as being crucially involved in conceptual processing.

Nonword repetition (NWR) has been a widely used measure of language-learning ability in children with and without language disorders. Although NWR tasks have been created for a variety of languages, minimal attention has been given to Asian tonal languages. This study introduces a new set of NWR stimuli for Vietnamese. The stimuli include 20 items ranging in length from one to four syllables. The items consist of dialect-neutral phonemes in consonant-vowel (CV) and CVC sequences that follow the phonotactic constraints of the language. They were rated high on wordlikeness and have comparable position segments and biphone probabilities across stimulus lengths. We validated the stimuli with a sample of 59 typically developing Vietnamese-English bilingual children, ages 5 to 8. The stimuli exhibited the expected age and length effects commonly found in NWR tasks: Older children performed better on the task than younger children, and longer items were more difficult to repeat than shorter items. We also compared different scoring systems in order to examine the individual phoneme types (consonants, vowels, and tones) and composite scores (proportions of phonemes correct, with and without tone). The study demonstrates careful construction and validation of the stimuli, and future directions are discussed.

textcopyright} 2017, The Author(s). Adults need to be able to process infants' emotional expressions accurately to respond appropriately and care for infants. However, research on processing of the emotional expressions of infant faces is hampered by the lack of validated stimuli. Although many sets of photographs of adult faces are available to researchers, there are no corresponding sets of photographs of infant faces. We therefore developed and validated a database of infant faces, which is available via e-mail request. Parents were recruited via social media and asked to send photographs of their infant (0–12 months of age) showing positive, negative, and neutral facial expressions. A total of 195 infant faces were obtained and validated. To validate the images, student midwives and nurses (n = 53) and members of the general public (n = 18) rated each image with respect to its facial expression, intensity of expression, clarity of expression, genuineness of expression, and valence. On the basis of these ratings, a total of 154 images with rating agreements of at least 75{\%} were included in the final database. These comprise 60 photographs of positive infant faces, 54 photographs of negative infant faces, and 40 photographs of neutral infant faces. The images have high criterion validity and good test–retest reliability. This database is therefore a useful and valid tool for researchers.

Using appropriate stimuli to evoke emotions is especially important for researching emotion. Psychologists have provided several standardized affective stimulus databases-such as the International Affective Picture System (IAPS) and the Nencki Affective Picture System (NAPS) as visual stimulus databases, as well as the International Affective Digitized Sounds (IADS) and the Montreal Affective Voices as auditory stimulus databases for emotional experiments. However, considering the limitations of the existing auditory stimulus database studies, research using auditory stimuli is relatively limited compared with the studies using visual stimuli. First, the number of sample sounds is limited, making it difficult to equate across emotional conditions and semantic categories. Second, some artificially created materials (music or human voice) may fail to accurately drive the intended emotional processes. Our principal aim was to expand existing auditory affective sample database to sufficiently cover natural sounds. We asked 207 participants to rate 935 sounds (including the sounds from the IADS-2) using the Self-Assessment Manikin (SAM) and three basic-emotion rating scales. The results showed that emotions in sounds can be distinguished on the affective rating scales, and the stability of the evaluations of sounds revealed that we have successfully provided a larger corpus of natural, emotionally evocative auditory stimuli, covering a wide range of semantic categories. Our expanded, standardized sound sample database may promote a wide range of research in auditory systems and the possible interactions with other sensory modalities, encouraging direct reliable comparisons of outcomes from different researchers in the field of psychology.

Orthography-semantics consistency (OSC) is a measure that quantifies the degree of semantic relatedness between a word and its orthographic relatives. OSC is computed as the frequency-weighted average semantic similarity between the meaning of a given word and the meanings of all the words containing that very same orthographic string, as captured by distributional semantic models. We present a resource including optimized estimates of OSC for 15,017 English words. In a series of analyses, we provide a progressive optimization of the OSC variable. We show that computing OSC from word-embeddings models (in place of traditional count models), limiting preprocessing of the corpus used for inducing semantic vectors (in particular, avoiding part-of-speech tagging and lemmatization), and relying on a wider pool of orthographic relatives provide better performance for the measure in a lexical-processing task. We further show that OSC is an important and significant predictor of reaction times in visual word recognition and word naming, one that correlates only weakly with other psycholinguistic variables (e.g., family size, word frequency), indicating that it captures a novel source of variance in lexical access. Finally, some theoretical and methodological implications are discussed of adopting OSC as one of the predictors of reaction times in studies of visual word recognition.

The rapid expansion of the Internet and the availability of vast repositories of natural text provide researchers with the immense opportunity to study human reactions, opinions, and behavior on a massive scale. To help researchers take advantage of this new frontier, the present work introduces and validates the Evaluative Lexicon 2.0 (EL 2.0)—a quantitative linguistic tool that specializes in the measurement of the emotionality of individuals' evaluations in text. Specifically, the EL 2.0 utilizes natural language to measure the emotionality, extremity, and valence of evaluative reactions and attitudes. The present article describes how we used a combination of 9 million real-world online reviews and over 1,500 participant judges to construct the EL 2.0 and an additional 5.7 million reviews to validate it. To assess its unique value, the EL 2.0 is compared with two other prominent text analysis tools—LIWC and Warriner et al.'s (Behavior Research Methods, 45, 1191–1207, 2013) wordlist. The EL 2.0 is comparatively distinct in its ability to measure emotionality and explains a significantly greater proportion of the variance in individuals' evaluations. The EL 2.0 can be used with any data that involve speech or writing and provides researchers with the opportunity to capture evaluative reactions both in the laboratory and “in the wild.” The EL 2.0 wordlist and normative emotionality, extremity, and valence ratings are freely available from www.evaluativelexicon.com.

This article describes the method used to build the Basque Verb Index (BVI), a corpus-based lexicon. The BVI is the result of semiautomatic annotation of the EPEC corpus with verb predicate information, following the PropBank-VerbNet model. The method presented is the product of a deep study of the syntactic–semantic behaviour of verbs in EPEC-RolSem (the EPEC corpus tagged with verb predicate information). During the process of annotating EPEC-RolSem, we have identified and stored in the BVI lexicon the different role-patterns associated with all verbs appearing in the corpus. In addition, each entry in the BVI is linked to the corresponding verb entry in well-known resources such as PropBank, VerbNet, WordNet and FrameNet. We have also implemented a tool called e-ROLda to facilitate the process of looking up verb patterns in the BVI and examples in EPEC-RolSem as a basis for future studies.

We present word prevalence data for 61,858 English words. Word prevalence refers to the number of people who know the word. The measure was obtained on the basis of an online crowdsourcing study involving over 220,000 people. Word prevalence data are useful for gauging the difficulty of words and, as such, for matching stimulus materials in experimental conditions or selecting stimulus materials for vocabulary tests. Word prevalence also predicts word processing times, over and above the effects of word frequency, word length, similarity to other words, and age of acquisition, in line with previous findings in the Dutch language.

Using the megastudy approach, we report a new database (MEGALEX) of visual and auditory lexical decision times and accuracy rates for tens of thousands of words. We collected visual lexical decision data for 28,466 French words and the same number of pseudowords, and auditory lexical decision data for 17,876 French words and the same number of pseudowords (synthesized tokens were used for the auditory modality). This constitutes the first large-scale database for auditory lexical decision, and the first database to enable a direct comparison of word recognition in different modalities. Different regression analyses were conducted to illustrate potential ways to exploit this megastudy database. First, we compared the proportions of variance accounted for by five word frequency measures. Second, we conducted item-level regression analyses to examine the relative importance of the lexical variables influencing performance in the different modalities (visual and auditory). Finally, we compared the similarities and differences between the two modalities. All data are freely available on our website ( https://sedufau.shinyapps.io/megalex/ ) and are searchable at www.lexique.org , inside the Open Lexique search engine.

This article describes a family of dependency treebanks of early attestations of Indo-European languages originating in the parallel treebank built by the members of the project pragmatic resources in old Indo-European languages. The treebanks all share a set of open-source software tools, including a web annotation interface, and a set of annotation schemes and guidelines developed especially for the project languages. The treebanks use an enriched dependency grammar scheme complemented by detailed morphological tags, which have proved sufficient to give detailed descriptions of these richly inflected languages, and which have been easy to adapt to new languages. We describe the tools and annotation schemes and discuss some challenges posed by the various languages that have been annotated. We also discuss problems with tokenisation, sentence division and lemmatisation, commonly encountered in ancient and mediaeval texts, and challenges associated with low levels of standardisation and ongoing morphological and syntactic change.

Standardized pictorial stimuli and predictors of successful picture naming are not readily available for Gulf Arabic. On the basis of data obtained from Qatari Arabic, a variety of Gulf Arabic, the present study provides norms for a set of 319 object pictures and a set of 141 action pictures. Norms were collected from healthy speakers, using a picture-naming paradigm and rating tasks. Norms for naming latencies, name agreement, visual complexity, image agreement, imageability, age of acquisition, and familiarity were established. Furthermore, the database includes other intrinsic factors, such as syllable length and phoneme length. It also includes orthographic frequency values (extracted from Aralex; Boudelaa {\&} Marslen-Wilson, 2010). These factors were then examined for their impact on picture-naming latencies in object- and action-naming tasks. The analysis showed that the primary determinants of naming latencies in both nouns and verbs are (in descending order) image agreement, name agreement, familiarity, age of acquisition, and imageability. These results indicate no evidence that noun- and verb-naming processes in Gulf Arabic are influenced in different ways by these variables. This is the first database for Gulf Arabic, and therefore the norms collected from the present study will be of paramount importance for researchers and clinicians working with speakers of this variety of Arabic. Due to the similarity of the Arabic varieties spoken in the Gulf, these different varieties are grouped together under the label "Gulf Arabic" in the literature. The normative databases and the standardized pictures from this study can be downloaded from http://qufaculty.qu.edu.qa/tariq-khwaileh/download-center/ .

We report a new multidimensional measure of visual complexity (GraphCom) that captures variability in the complexity of graphs within and across writing systems. We applied the measure to 131 written languages, allowing comparisons of complexity and providing a basis for empirical testing of GraphCom. The measure includes four dimensions whose value in capturing the different visual properties of graphs had been demonstrated in prior reading research—(1) perimetric complexity, sensitive to the ratio of a written form to its surrounding white space (Pelli, Burns, Farell, {\&} Moore-Page, 2006); (2) number of disconnected components, sensitive to discontinuity (Gibson, 1969); (3) number of connected points, sensitive to continuity (Lanthier, Risko, Stolz, {\&} Besner, 2009); and (4) number of simple features, sensitive to the strokes that compose graphs (Wu, Zhou, {\&} Shu, 1999). In our analysis of the complexity of 21,550 graphs, we (a) determined the complexity variation across writing systems along each dimension, (b) examined the relationships among complexity patterns within and across writing systems, and (c) compared the dimensions in their abilities to differentiate the graphs from different writing systems, in order to predict human perceptual judgments (n = 180) of graphs with varying complexity. The results from the computational and experimental comparisons showed that GraphCom provides a measure of graphic complexity that exceeds previous measures in its empirical validation. The measure can be universally applied across writing systems, providing a research tool for studies of reading and writing.

We describe the Multilanguage Written Picture Naming Dataset. This gives trial-level data and time and agreement norms for written naming of the 260 pictures of everyday objects that compose the colorized Snodgrass and Vanderwart picture set (Rossion {\&} Pourtois in Perception, 33, 217–236, 2004). Adult participants gave keyboarded responses in their first language under controlled experimental conditions (N = 1,274, with subsamples responding in Bulgarian, Dutch, English, Finnish, French, German, Greek, Icelandic, Italian, Norwegian, Portuguese, Russian, Spanish, and Swedish). We measured the time to initiate a response (RT) and interkeypress intervals, and calculated measures of name and spelling agreement. There was a tendency across all languages for quicker RTs to pictures with higher familiarity, image agreement, and name frequency, and with higher name agreement. Effects of spelling agreement and effects on output rates after writing onset were present in some, but not all, languages. Written naming therefore shows name retrieval effects that are similar to those found in speech, but our findings suggest the need for cross-language comparisons as we seek to understand the orthographic retrieval and/or assembly processes that are specific to written output.

This article presents the Provo Corpus, a corpus of eye-tracking data with accompanying predictability norms. The predictability norms for the Provo Corpus differ from those of other corpora. In addition to traditional cloze scores that estimate the predictability of the full orthographic form of each word, the Provo Corpus also includes measures of the predictability of the morpho-syntactic and semantic information for each word. This makes the Provo Corpus ideal for studying predictive processes in reading. Some analyses using these data have previously been reported elsewhere (Luke {\&} Christianson, 2016). The Provo Corpus is available for download on the Open Science Framework, at https://osf.io/sjefs .

In this article, we present StimulStat – a lexical database for the Russian language in the form of a web application. The database contains more than 52,000 of the most frequent Russian lemmas and more than 1.7 million word forms derived from them. These lemmas and forms are characterized according to more than 70 properties that were demonstrated to be relevant for psycholinguistic research, including frequency, length, phonological and grammatical properties, orthographic and phonological neighborhood frequency and size, grammatical ambiguity, homonymy and polysemy. Some properties were retrieved from various dictionaries and are presented collectively in a searchable form for the first time, the others were computed specifically for the database. The database can be accessed freely at http://stimul.cognitivestudies.ru. {\textcopyright} 2017, Psychonomic Society, Inc.

This article reports the construction of a multimodal annotated database of spoken discourse and co-verbal gestures by native healthy speakers of Cantonese and individuals with language impairment: the Cantonese AphasiaBank. This corpus was established as a foundation for aphasiologists and clinicians to use in designing and conducting research investigations into theoretical and clinical issues related to acquired language disorders in Chinese. Details in terms of the purpose, structure, and levels of annotation of the database (containing part-of-speech-annotated orthographic transcripts with Romanization and the corresponding videos) are described. The discussion presents the challenges of building a spoken database of a language that is not linguistically well-researched and that does not have a standardized written form for many of its lexical items, as well as presenting how these issues were addressed. Most importantly, the article highlights the potential of Cantonese AphasiaBank as a powerful research tool for linguists and psycholinguists.

The present study provides normative measures for a new stimulus set of images consisting of 225 everyday objects, each depicted both as a photograph and a matched clipart image generated directly from the photograph (450 images total). The clipart images preserve the same scale, shape, orientation, and general color features as the corresponding photographs. Various norms (modal name and verb agreement measures, picture-name agreement, familiarity, visual complexity, and image agreement) were collected separately for each image type and in two different contexts: online (using Mechanical Turk) and in the laboratory. We discuss similarities and differences in the normative measures according to both image type and experimental context. The full set of norms is provided in the supplemental materials.

textcopyright} 2018 Psychonomic Society, Inc. In language production research, the latency with which speakers produce a spoken response to a stimulus and the onset and offset times of words in longer utterances are key dependent variables. Measuring these variables automatically often yields partially incorrect results. However, exact measurements through the visual inspection of the recordings are extremely time-consuming. We present AlignTool, an open-source alignment tool that establishes preliminarily the onset and offset times of words and phonemes in spoken utterances using Praat, and subsequently performs a forced alignment of the spoken utterances and their orthographic transcriptions in the automatic speech recognition system MAUS. AlignTool creates a Praat TextGrid file for inspection and manual correction by the user, if necessary. We evaluated AlignTool's performance with recordings of single-word and four-word utterances as well as semi-spontaneous speech. AlignTool performs well with audio signals with an excellent signal-to-noise ratio, requiring virtually no corrections. For audio signals of lesser quality, AlignTool still is highly functional but its results may require more frequent manual corrections. We also found that audio recordings including long silent intervals tended to pose greater difficulties for AlignTool than recordings filled with speech, which AlignTool analyzed well overall. We expect that by semi-automatizing the temporal analysis of complex utterances, AlignTool will open new avenues in language production research.

Automatic speech recognition (ASR) has gained significant improvement for major languages such as English and Chinese, partly due to the emergence of deep neural networks (DNN) and large amount of training data. For minority languages, however, the progress is largely behind the main stream. A particularly obstacle is that there are almost no large-scale speech databases for minority languages, and the only few databases are held by some institutes as private properties, far from open and standard, and very few are free. Besides the speech database, phonetic and linguistic resources are also scarce, including phone set, lexicon, and language model. In this paper, we publish a speech database in Kazakh, a major minority language in the western China. Accompanying this database, a full set of phonetic and linguistic resources are also published, by which a full-fledged Kazakh ASR system can be constructed. We will describe the recipe for constructing a baseline system, and report our present results. The resources are free for research institutes and can be obtained by request. The publication is supported by the M2ASR project supported by NSFC, which aims to build multilingual ASR systems for minority languages in China.

In the present work, we introduce the Miami University Deception Detection Database (MU3D), a free resource containing 320 videos of target individuals telling truths and lies. Eighty (20 Black female, 20 Black male, 20 White female, and 20 White male) different targets were recorded speaking honestly and dishonestly about their social relationships. Each target generated four different videos (i.e., positive truth, negative truth, positive lie, negative lie), yielding 320 videos fully crossing target race, target gender, statement valence, and statement veracity. These videos were transcribed by trained research assistants and evaluated by na{\"{i}}ve raters. Descriptive analyses of the video characteristics (e.g., length) and subjective ratings (e.g., target attractiveness) are provided. The stimuli and an information codebook can be accessed free of charge for academic research purposes from http://hdl.handle.net/2374.MIA/6067. The MU3D offers scholars the ability to conduct research using standardized stimuli that can aid in building more comprehensive theories of interpersonal sensitivity, enhance replication among labs, facilitate the use of signal detection analyses, and promote consideration of race, gender, and their interactive effects in deception detection research.

Humor ratings are provided for 4,997 English words collected from 821 participants using an online crowd-sourcing platform. Each participant rated 211 words on a scale from 1 (humorless) to 5 (humorous). To provide for comparisons across norms, words were chosen from a set common to a number of previously collected norms (e.g., arousal, valence, dominance, concreteness, age of acquisition, and reaction time). The complete dataset provides researchers with a list of humor ratings and includes information on gender, age, and educational differences. Results of analyses show that the ratings have reliability on a par with previous ratings and are not well predicted by existing norms.

We present here emoFinder ( http://usc.es/pcc/emofinder ), a Web-based search engine for Spanish word properties taken from different normative databases. The tool incorporates several subjective word properties for 16,375 distinct words. Although it focuses particularly on normative ratings for emotional dimensions (e.g., valence and arousal) and discrete emotional categories (fear, disgust, anger, happiness, and sadness), it also makes available ratings for other word properties that are known to affect word processing (e.g., concreteness, familiarity, contextual availability, and age of acquisition). The tool provides two main functionalities: Users can search for words matched on specific criteria with regard to the selected properties, or users can obtain the properties for a set of words. The output from emoFinder is highly customizable and can be accessed online or exported to a computer. The tool architecture is easily scalable, so that it can be updated to include word properties from new Spanish normative databases as they become available.

The use of emoticons and emoji is increasingly popular across a variety of new platforms of online communication. They have also become popular as stimulus materials in scientific research. However, the assumption that emoji/emoticon users' interpretations always correspond to the developers'/researchers' intended meanings might be misleading. This article presents subjective norms of emoji and emoticons provided by everyday users. The Lisbon Emoji and Emoticon Database (LEED) comprises 238 stimuli: 85 emoticons and 153 emoji (collected from iOS, Android, Facebook, and Emojipedia). The sample included 505 Portuguese participants recruited online. Each participant evaluated a random subset of 20 stimuli for seven dimensions: aesthetic appeal, familiarity, visual complexity, concreteness, valence, arousal, and meaningfulness. Participants were additionally asked to attribute a meaning to each stimulus. The norms obtained include quantitative descriptive results (means, standard deviations, and confidence intervals) and a meaning analysis for each stimulus. We also examined the correlations between the dimensions and tested for differences between emoticons and emoji, as well as between the two major operating systems-Android and iOS. The LEED constitutes a readily available normative database (available at www.osf.io/nua4x ) with potential applications to different research domains.

Phonotactic probability refers to the frequency with which phonological segments and sequences of phonological segments occur in words in a given language. We describe one method of estimating phonotactic probabilities based on words in American English. These estimates of phonotactic probability have been used in a number of previous studies and are now being made available to other researchers via a Web-based interface. Instructions for using the interface, as well as details regarding how the measures were derived, are provided in the present article. The Phonotactic Probability Calculator can be accessed at http://www.people.ku.edu/-mvitevit/PhonoProbHome.html.

Sentiment information about social media posts is increasingly considered an important resource for customer segmentation, market understanding, and tackling other socio-economic issues. However, sentiment in social media is difficult to measure since user-generated content is usually short and informal. Although many traditional sentiment analysis methods have been proposed, identifying slang sentiment words remains a challenging task for practitioners. Though some slang words are available in existing sentiment lexicons, with new slang being generated with emerging memes, a dedicated lexicon will be useful for researchers and practitioners. To this end, we propose to build a slang sentiment dictionary to aid sentiment analysis. It is laborious and time-consuming to collect a comprehensive list of slang words and label the sentiment polarity. We present an approach to leverage web resources to construct a Slang Sentiment Dictionary (SlangSD) that is easy to expand. SlangSD is publicly available for research purposes. We empirically show the advantages of using SlangSD, the newly-built slang sentiment word dictionary for sentiment classification, and provide examples demonstrating its ease of use with a sentiment analysis system.

The paper presents the results of the Janes project, which aimed to develop language resources and tools for Slovene user generated content. The paper first describes the 200 million word Janes corpus, containing tweets, forum posts, news comments, user and talk pages from Wikipedia, and blogs and blog comments, where each text is accompanied by rich metadata. The developed processing tools for Slovene user generated content are presented next, which include a tokeniser, word-normaliser, part-of-speech tagger and lemmatiser, and a named entity recogniser. A set of manually annotated datasets was also produced, both for tool training as well as for linguistic research. The developed resources and tools are made publicly available under Creative Commons licences in the repository of the CLARIN.SI research infrastructure and on GitHub, while the corpora are also available through the CLARIN.SI concordancers.

Most current models of research on emotion recognize valence (how pleasant a stimulus is) and arousal (the level of activation or intensity that a stimulus elicits) as important components in the classification of affective experiences (Barrett, 1998; Kuppens, Tuerlinckx, Russell, {\&} Barrett, 2012). Here we present a set of norms for valence and arousal for a very large set of Spanish words, including items from a variety of frequencies, semantic categories, and parts of speech, including a subset of conjugated verbs. In this regard, we found that there were significant but very small differences between the ratings for conjugations of the same verb, validating the practice of applying the ratings for infinitives to all derived forms of the verb. Our norms show a high degree of reliability and are strongly correlated with those of Redondo, Fraga, Padr{\'{o}}n, and Comesa{\~{n}}a's (2007) Spanish version of the influential Affective Norms for English Words (Bradley {\&} Lang, 1999), as well as those from Warriner, Kuperman, and Brysbaert (2013), the largest available set of emotional norms for English words. Additionally, we included measures of word prevalence-that is, the percentage of participants that knew a particular word-for each variable (Keuleers, Stevens, Mandera, {\&} Brysbaert, 2015). Our large set of norms in Spanish not only will facilitate the creation of stimuli and the analysis of texts in that language, but also will be useful for cross-language comparisons and research on emotional aspects of bilingualism. The norms can be downloaded and available as a supplementary materials to this article.

During social communication, words and sentences play a critical role in the expression of emotional meaning. The Minho Affective Sentences (MAS) were developed to respond to the lack of a standardized sentence battery with normative affective ratings: 192 neutral, positive, and negative declarative sentences were strictly controlled for psycholinguistic variables such as numbers of words and letters and per-million word frequency. The sentences were designed to represent examples of each of the five basic emotions (anger, sadness, disgust, fear, and happiness) and of neutral situations. These sentences were presented to 536 participants who rated the stimuli using both dimensional and categorical measures of emotions. Sex differences were also explored. Additionally, we probed how personality, empathy, and mood from a subset of 40 participants modulated the affective ratings. Our results confirmed that the MAS affective norms are valid measures to guide the selection of stimuli for experimental studies of emotion. The combination of dimensional and categorical ratings provided a more fine-grained characterization of the affective properties of the sentences. Moreover, the affective ratings of positive and negative sentences were not only modulated by participants' sex, but also by individual differences in empathy and mood state. Together, our results indicate that, in their quest to reveal the neurofunctional underpinnings of verbal emotional processing, researchers should consider not only the role of sex, but also of interindividual differences in empathy and mood states, in responses to the emotional meaning of sentences.

We have developed and tested 144 compound remote associate problems. Across eight experiments, 289 participants were given four time limits (2 sec, 7 sec, 15 sec, or 30 sec) for solving each problem. This paper provides a brief overview of the problems and normative data regarding the percentage of participants solving, and mean time-to-solution for, each problem at each time limit. These normative data can be used in selecting problems on the basis of difficulty or mean time necessary for reaching a solution.

This paper presents a rule-based approach for generating a large phonetic database for Romanian. The knowledge base is developed by means of the GRAALAN (Grammar Abstract Language) system. By inspecting dictionaries and corpora, we generate a phonetic database over 100,000 lemmas. Our database has a high degree of accuracy ensured by our rule-based method applied for generating phonetic transcriptions.

The BT Archives house the records of British Telecom, the world's oldest telecommunications company, which traces its history back to the formation of the Electric Telegraphy Company in 1846. Prior to its privatisation in 1984, BT was a public corporation (and before that a government department) and as a result all of the pre-privatisation material in the archives is in the public domain, making it ideal for academic research. Despite this legal availability, however, the physical availability of material in the archive was limited to two days a week in an archive space in Holborn, London. In 2011 the ‘New Connections' project was set up with the aim of making around half a million items from the public archives of British Telecom available in a new digital archive. As part of ‘New Connections', three academic research projects were funded, one of which was the creation and analysis of the British Telecom Correspondence Corpus (BTCC). The era that the archive covers makes it a potentially fascinating source of data for the linguistic study of business correspondence. The mid-nineteenth to latetwentieth century is a crucial period in the development of English business correspondence as the amount of business being conducted by letter increased massively during this period as a result of the Industrial Revolution, the introduction of the Penny Post, and increased access to education both in schools and through composition grammar guides. Despite its importance in the development of business correspondence, this period has received relatively little attention. The aim of constructing the British Telecom Correspondence Corpus was to start addressing this gap in available linguistic data and enable studies into the development of business correspondence from the mid-nineteenth to late-twentieth century.

Here we report on MELD-SCH (MEgastudy of Lexical Decision in Simplified CHinese), a dataset that contains the lexical decision data of 1,020 one-character, 10,022 two-character, 949 three-character, and 587 four-character simplified Chinese words obtained from 504 native Chinese users. It also includes a number of word-level and character-level variables. Analyses showed that the reliability of the dataset is satisfactory, as indicated by split-half correlations and comparisons with other datasets. Item-based regression showed that both word-level and character-level variables contributed significantly to the reaction times and error rates of lexical decision. Moreover, we discovered a U-shape relationship between word-length and reaction times, which has not been reported in Chinese before. MELD-SCH can facilitate research in Chinese word recognition by providing high quality normative data and information of different linguistic variables. It also encourages researchers to extend their empirical findings, which are mostly based on one-character and two-character words, to words of different lengths.

This study introduces a corpus of 260 naturalistic human nonlinguistic vocalizations representing nine emotions: amusement, anger, disgust, effort, fear, joy, pain, pleasure, and sadness. The recognition accuracy in a rating task varied greatly per emotion, from {\textless}40{\{}{\%}{\}} for joy and pain, to {\textgreater}70{\{}{\%}{\}} for amusement, pleasure, fear, and sadness. In contrast, the raters' linguistic--cultural group had no effect on recognition accuracy: The predominantly English-language corpus was classified with similar accuracies by participants from Brazil, Russia, Sweden, and the UK/USA. Supervised random forest models classified the sounds as accurately as the human raters. The best acoustic predictors of emotion were pitch, harmonicity, and the spacing and regularity of syllables. This corpus of ecologically valid emotional vocalizations can be filtered to include only sounds with high recognition rates, in order to study reactions to emotional stimuli of known perceptual types (reception side), or can be used in its entirety to study the association between affective states and vocal expressions (production side).

This article introduces GECO, the Ghent Eye-Tracking Corpus, a monolingual and bilingual corpus of the eyetracking data of participants reading a complete novel. English monolinguals and Dutch–English bilinguals read an entire novel, which was presented in paragraphs on the screen. The bilinguals read half of the novel in their first language, and the other half in their second language. In this article, we describe the distributions and descriptive statistics of the most important reading time measures for the two groups of participants. This large eyetracking corpus is perfectly suited for both exploratory purposes and more directed hypothesis testing, and it can guide the formulation of ideas and theories about naturalistic reading processes in a meaningful context. Most importantly, this corpus has the potential to evaluate the generalizability of monolingual and bilingual language theories and models to the reading of long texts and narratives. The corpus is freely available at http://expsy.ugent.be/downloads/geco.

The discrete emotion theory proposes that affective experiences can be reduced to a limited set of universal "basic" emotions, most commonly identified as happiness, sadness, anger, fear, and disgust. Here we present norms for 10,491 Spanish words for those five discrete emotions collected from a total of 2,010 native speakers, making it the largest set of norms for discrete emotions in any language to date. When used in conjunction with the norms from Hinojosa, Mart{\'{i}}nez-Garc{\'{i}}a et al. (Behavior Research Methods, 48, 272-284, 2016) and Ferr{\'{e}}, Guasch, Mart{\'{i}}nez-Garc{\'{i}}a, Fraga, {\&} Hinojosa (Behavior Research Methods, 49, 1082-1094, 2017), researchers now have access to ratings of discrete emotions for 13,633 Spanish words. Our norms show a high degree of inter-rater reliability and correlate highly with those from Ferr{\'{e}} et al. (2017). Our exploration of the relationship between the five discrete emotions and relevant lexical and emotional variables confirmed findings of previous studies conducted with smaller datasets. The availability of such large set of norms will greatly facilitate the study of emotion, language and related fields. The norms are available as supplementary materials to this article.

In this article we present Curras, the first morphologically annotated corpus of the Palestinian Arabic dialect. Palestinian Arabic is one of the many primarily spoken dialects of the Arabic language. Arabic dialects are generally under-resourced compared to Modern Standard Arabic, the primarily written and official form of Arabic. We start in the article with a background description that situates Palestinian Arabic linguistically and historically and compares it to Modern Standard Arabic and Egyptian Arabic in terms of phonological, morphological, orthographic, and lexical variations. We then describe the methodology we developed to collect Palestinian Arabic text to guarantee a variety of representative domains and genres. We also discuss the annotation process we used, which extended previous efforts for annotation guideline development, and utilized existing automatic annotation solutions for Standard Arabic and Egyptian Arabic. The annotation guidelines and annotation meta-data are described in detail. The Curras Palestinian Arabic corpus consists of more than 56 K tokens, which are annotated with rich morphological and lexical features. The inter-annotator agreement results indicate a high degree of consistency.

This article introduces the MC4WEPS corpus, a new resource for evaluating Web people search disambiguation tasks, and describes its design, collection and annotation process, the agreement between the different annotators, and finally introduces a baseline evaluation. This corpus is built by compiling multilingual search engines results where the queries are person names. Proper noun disambiguation is an open problem in natural language ambiguity resolution and, specifically, resolving the ambiguity of person names in Web search results is still a challenging problem. However, state-of-the-art approaches have been evaluated only with monolingual web page collections. The MC4WEPS corpus aims to provide the research community with a reference corpus for the task of disambiguating search engine results where the query is a person name shared by homonymous individuals. The features of this new corpus stand out from existing corpora for the same task, namely multilingualism and inclusion of social networking websites. These characteristics make it more representative of a real search scenario, especially for evaluating person name disambiguation in a multilingual context. The article also includes detailed information about the format and the availability of the corpus.

We introduce the Open Affective Standardized Image Set (OASIS), an open-access online stimulus set containing 900 color images depicting a broad spectrum of themes, including humans, animals, objects, and scenes, along with normative ratings on two affective dimensions-valence (i.e., the degree of positive or negative affective response that the image evokes) and arousal (i.e., the intensity of the affective response that the image evokes). The OASIS images were collected from online sources, and valence and arousal ratings were obtained in an online study (total N = 822). The valence and arousal ratings covered much of the circumplex space and were highly reliable and consistent across gender groups. OASIS has four advantages: (a) the stimulus set contains a large number of images in four categories; (b) the data were collected in 2015, and thus OASIS features more current images and reflects more current ratings of valence and arousal than do existing stimulus sets; (c) the OASIS database affords users the ability to interactively explore images by category and ratings; and, most critically, (d) OASIS allows for free use of the images in online and offline research studies, as they are not subject to the copyright restrictions that apply to the International Affective Picture System. The OASIS images, along with normative valence and arousal ratings, are available for download from www.benedekkurdi.com/{\#}oasis or https://db.tt/yYTZYCga .

This article provides norms for general taboo, personal taboo, insult, valence, and arousal for 672 Dutch words, including 202 taboo words. Norms were collected using a 7-point Likert scale and based on ratings by psychology students from the Erasmus University Rotterdam in The Netherlands. The sample consisted of 87 psychology students (58 females, 29 males). We obtained high reliability based on split-half analyses. Our norms show high correlations with arousal and valence ratings collected by another Dutch word-norms study (Moors et al.,, Behavior Research Methods, 45, 169-177, 2013). Our results show that the previously found qua-dratic relation (i.e., U-shaped pattern) between valence and arousal also holds when only taboo words are considered. Additionally, words rated high on taboo tended to be rated low on valence, but some words related to sex rated high on both taboo and valence. Words that rated high on taboo rated high on insult, again with the exception of words related to sex many of which rated low on insult. Finally, words rated high on taboo and insult rated high on arousal. The Dutch Taboo Norms (DTN) database is a useful tool for researchers interested in the effects of taboo words on cognitive processing. The data associated with this paper can be accessed via the Open Science Framework (https://osf.io/vk782/).

This paper presents the methodology, design principles and detailed evaluation of a new freely available multilayer corpus, collected and edited via classroom annotation using collaborative software. After briefly discussing corpus design for open, extensible corpora, five classroom annotation projects are presented, covering structural markup in TEI XML, multiple part of speech tagging, constituent and dependency parsing, information structural and coreference annotation, and Rhetorical Structure Theory analysis. Layers are inspected for annotation quality and together they coalesce to form a richly annotated corpus that can be used to study the interactions between different levels of linguistic description. The evaluation gives an indication of the expected quality of a corpus created by students with relatively little training. A multifactorial example study on lexical NP coreference likelihood is also presented, which illustrates some applications of the corpus. The results of this project show that high quality, richly annotated resources can be created effectively as part of a linguistics curriculum, opening new possibilities not just for research, but also for corpora in linguistics pedagogy.

In experimental contexts, affect-related word lists have been widely applied when examining how cognitive processes interact with emotional processes. These lists, however, present limitations when studying the relation between emotion and cognitive processes such as time and number processing because affective words do not inherently contain time or quantity information. Live events, in contrast, are experienced by an observer and therefore inherently carry affect information. Unfortunately, existing life-event lists and inventories have been largely applied within clinical contexts as diagnostic tools, and therefore are not suitable for many experimental contexts because they do not contain a balanced number of reliably positive, negative, and neutral life events. In Experiment 1, we create a standardized affect-related life-events list with 171 positive, negative, and neutral affect-related life events. In Experiment 2, we show that strength of affect and significance of the event are integral dimensions, suggesting that these two features are difficult to separate perceptually. The implications of these findings and some potential future applications of the created life-events list are discussed.

This paper presents the Stars2 corpus of definite descriptions for referring expression generation (REG). The corpus was produced in collaborative communication involving speaker-hearer pairs, and includes situations of reference that are arguably under-represented in similar work. Stars2 is intended as an incremental contribution to the research in REG and related fields, and it may be used both as training/test data for algorithms of this kind, and also to gain further insights into reference phenomena in general, with a particular focus on the issue of attribute choice in referential overspecification.

Given the vast amounts of data available in digitised textual form, it is important to provide mechanisms that allow users to extract nuggets of relevant information from the ever growing volumes of potentially important documents. Text mining techniques can help, through their ability to automatically extract relevant event descriptions, which link entities with situations described in the text. However, correct and complete interpretation of these event descriptions is not possible without considering additional contextual information often present within the surrounding text. This information, which we refer to as meta-knowledge, can include (but is not restricted to) the modality, subjectivity, source, polarity and specificity of the event. We have developed a meta-knowledge annotation scheme specifically tailored for news events, which includes six aspects of event interpretation. We have applied this annotation scheme to the ACE 2005 corpus, which contains 599 documents from various written and spoken news sources. We have also identified and annotated the words and phrases evoking the different types of meta-knowledge. Evaluation of the annotated corpus shows high levels of inter-annotator agreement for five meta-knowledge attributes, and moderate level of agreement for the sixth attribute. Detailed analysis of the annotated corpus has revealed further insights into the expression mechanisms of different types of meta-knowledge, their relative frequencies and mutual correlations.

In the vast literature exploring learning, many studies have used paired-associate stimuli, despite the fact that real-world learning involves many different types of information. One of the most popular materials used in studies of learning has been a set of Swahili-English word pairs for which Nelson and Dunlosky (Memory 2; 325-335, 1994) published recall norms two decades ago. These norms involved use of the Swahili words as cues to facilitate recall of the English translation. It is unclear whether cueing in the opposite direction (from English to Swahili) would lead to symmetric recall performance. Bilingual research has suggested that translation in these two different directions involves asymmetric links that may differentially impact recall performance, depending on which language is used as the cue (Kroll {\&} Stewart, Journal of Memory and Language 33; 149-174,1994). Moreover, the norms for these and many other learning stimuli have typically been gathered from college students. In the present study, we report recall accuracy and response time norms for Swahili words when they are cued by their English translations. We also report norms for a companion set of fact stimuli that may be used along with the Swahili-English word pairs to assess learning on a broader scale across different stimulus materials. Data were collected using Amazon's Mechanical Turk to establish a sample that was diverse in both age and ethnicity. These different, but related, stimulus sets will be applicable to studies of learning, metacognition, and memory in diverse samples.

As the cognitive neuroscience of metaphor has evolved, so too have the theoretical questions of greatest interest. To keep pace with these developments, in the present study we generated a large set of metaphoric and literal sentence pairs ideally suited to addressing the current methodological and conceptual needs of metaphor researchers. In particular, the need has emerged to distinguish metaphors along three dimensions: the grammatical class of their base terms, the sensorimotor features of their base terms, and the syntactic form in which the base terms appear. To meet this need, we generated nominal metaphors (and matched literal sentences) using entity nouns as the base terms, with the intention that they be used in concert with already published sets of predicate metaphors or nominal metaphors using event nouns. Using the results of three norming experiments, we provide 120 pairs of closely matched metaphoric and literal sentences that are characterized along 14 dimensions: 11 at the sentence level (length, frequency, concreteness, familiarity, naturalness, imageability, figurativeness, interpretability, ease of interpretation, valence, and valence judgment reaction time), and three related to the base term (visual, motion, and auditory imagery). These items extend previously published stimuli, filling an extant gap in metaphor research and allowing for tests of new behavioral and neural hypotheses about metaphor.

Our visual environment is not random, but follows compositional rules according to what objects are usually found where. Despite the growing interest in how such semantic and syntactic rules - a scene grammar - enable effective attentional guidance and object perception, no common image database containing highly-controlled object-scene modifications has been publically available. Such a database is essential in minimizing the risk that low-level features drive high-level effects of interest, which is being discussed as possible source of controversial study results. To generate the first database of this kind - SCEGRAM - we took photographs of 62 real-world indoor scenes in six consistency conditions that contain semantic and syntactic (both mild and extreme) violations as well as their combinations. Importantly, always two scenes were paired, so that an object was semantically consistent in one scene (e.g., ketchup in kitchen) and inconsistent in the other (e.g., ketchup in bathroom). Low-level salience did not differ between object-scene conditions and was generally moderate. Additionally, SCEGRAM contains consistency ratings for every object-scene condition, as well as object-absent scenes and object-only images. Finally, a cross-validation using eye-movements replicated previous results of longer dwell times for both semantic and syntactic inconsistencies compared to consistent controls. In sum, the SCEGRAM image database is the first to contain well-controlled semantic and syntactic object-scene inconsistencies that can be used in a broad range of cognitive paradigms (e.g., verbal and pictorial priming, change detection, object identification, etc.) including paradigms addressing developmental aspects of scene grammar. SCEGRAM can be retrieved for research purposes from http://www.scenegrammarlab.com/research/scegram-database/ .

[Correction Notice: An Erratum for this article was reported in Vol 49(3) of Behavior Research Methods (see record 2017-21867-030). In the original article, there was an error in the second sentence of Footnote 3 on page 175. The corrected sentence is present in the erratum.] The body-shape-related stimuli used in most body-image studies have several limitations (e.g., a lack of pilot validation procedures and the use of non-body-shape-related control/neutral stimuli). We therefore developed a database of 61 computer-generated body-only pictures of women, wherein bodies were methodically manipulated in terms of fatness versus thinness. Eighty-two young women assessed the pictures' attractiveness, beauty, harmony (valence ratings), and body shape (assessed on a thinness/fatness axis), providing normative data for valence and body shape ratings. First, stimuli manipulated for fatness versus thinness conveyed comparable emotional intensities regarding the valence and body shape ratings. Second, different subcategories of stimuli were obtained on the basis of variations in body shape and valence judgments. Fat and thin bodies were distributed into several subcategories depending on their valence ratings, and a subcategory containing stimuli that were neutral in terms of valence and body shape was identified. Interestingly, at a descriptive level, the thinness/fatness manipulations of the bodies were in a curvilinear relationship with the valence ratings: Thin bodies were not only judged as positive, but also as negative when their estimated body mass indexes (BMIs) decreased too much. Finally, convergent validity was assessed by exploring the impacts of body-image-related variables (BMI, thin-ideal internalization, and body dissatisfaction) on participants' judgments of the bodies. Valence judgments, but not body shape judgments, were influenced by the participants' levels of thin-ideal internalization and body dissatisfaction. Participants' BMIs did not significantly influence their judgments. Given these findings, this database contains relevant material that can be used in various fields, primarily for studies of body-image disturbance or eating disorders. (PsycINFO Database Record (c) 2017 APA, all rights reserved) (Source: journal abstract)

Age of acquisition (AoA) is an important variable in word recognition research. Up to now, nearly all psychology researchers examining the AoA effect have used ratings obtained from adult participants. An alternative basis for determining AoA is directly testing children's knowledge of word meanings at various ages. In educational research, scholars and teachers have tried to establish the grade at which particular words should be taught by examining the ages at which children know various word meanings. Such a list is available from Dale and O'Rourke's (1981) Living Word Vocabulary for nearly 44 thousand meanings coming from over 31 thousand unique word forms and multiword expressions. The present article relates these test-based AoA estimates to lexical decision times as well as to AoA adult ratings, and reports strong correlations between all of the measures. Therefore, test-based estimates of AoA can be used as an alternative measure.

This article provides semantic differential ratings of 1,469 concepts in Bengali, a language spoken by about 250 million individuals in eastern India and Bangladesh. These data were collected from 20 male and 20 female Calcutta respondents who rated stimuli on three culturally universal affective dimensions: evaluation–potency–activity (EPA). This study employs pan-respondent component analyses as a means of examining the respondents' usage of the standard EPA scales. The pan-respondent component analyses indicate that some respondents used the rating scales in unexpected ways, recording their feelings about one component of concepts' EPA with ratings on a scale intended to measure a different dimension. When scores were based only on respondents who used the scales appropriately, several interesting patterns were found. For respondents of both genders, potency scores have a curvilinear relation with evaluation, such that very good and very bad concepts are mostly seen as very potent, whereas evaluatively neutral concepts are seen as somewhat impotent or just slightly potent. A moderate linear correlation exists between activity and evaluation, and a modest positive relation exists between potency and activity. Gender correlations are high on evaluation, .93, but much lower for potency scores, with a correlation of .55, and even lower for activity, .30. In this article we examine several explanations for why scales denoting potency and activity were reinterpreted as indicating goodness by certain respondents, and consider the matter of including data collected from respondents who used scales in this way.

In everyday social interactions, people's facial expressions sometimes reflect genuine emotion (e.g., anger in response to a misbehaving child) and sometimes do not (e.g., smiling for a school photo). There is increasing theoretical interest in this distinction, but little is known about perceived emotion genuineness for existing facial expression databases. We present a new method for rating perceived genuineness using a neutral-midpoint scale (-7 = completely fake; 0 = don't know; +7 = completely genuine) that, unlike previous methods, provides data on both relative and absolute perceptions. Normative ratings from typically developing adults for five emotions (anger, disgust, fear, sadness, and happiness) provide three key contributions. First, the widely used Pictures of Facial Affect (PoFA; i.e., "the Ekman faces") and the Radboud Faces Database (RaFD) are typically perceived as not showing genuine emotion. Also, in the only published set for which the actual emotional states of the displayers are known (via self-report; the McLellan faces), percepts of emotion genuineness often do not match actual emotion genuineness. Second, we provide genuine/fake norms for 558 faces from several sources (PoFA, RaFD, KDEF, Gur, FacePlace, McLellan, News media), including a list of 143 stimuli that are event-elicited (rather than posed) and, congruently, perceived as reflecting genuine emotion. Third, using the norms we develop sets of perceived-as-genuine (from event-elicited sources) and perceived-as-fake (from posed sources) stimuli, matched on sex, viewpoint, eye-gaze direction, and rated intensity. We also outline the many types of research questions that these norms and stimulus sets could be used to answer.

Idiomatic expressions such as kick the bucket or go down a storm can differ on a number of internal features, such as familiarity, meaning, literality, and decomposability, and these types of features have been the focus of a number of normative studies. In this article, we provide normative data for a set of Bulgarian idioms and their English translations, and by doing so replicate in a Slavic language the relationships between the ratings previously found in Romance and Germanic languages. Additionally, we compared whether collecting these types of ratings in between-subjects or within-subjects designs affects the data and the conclusions drawn, and found no evidence that design type affects the final outcome. Finally, we present the results of a meta-analysis that summarizes the relationships found across the literature. As in many previous individual studies, we found that familiarity correlates with a number of other features; however, such studies have shown conflicting results concerning literality and decomposability ratings. The meta-analysis revealed reliable relationships of decomposability with a number of other measures, such as familiarity, meaning, and predictability. Conversely, literality was shown to have little to no relationship with any of the other subjective ratings. The implications for these relationships in the context of the wider experimental literature are discussed, with a particular focus on the importance of attaining familiarity ratings for each sample of participants in experimental work.

The Developmental Emotional Faces Stimulus Set (DEFSS) is designed to provide a standardized set of emotional stimuli that includes both child and adult faces, and that has been validated by participants across a wide range of ages. This article describes the creation and validation of the DEFSS, which includes 404 validated facial photographs of people between 8 and 30 years old displaying 5 different emotional expressions: happy, angry, fearful, sad, and neutral. The emotions in all photographs were identified correctly by 86{\%} of raters (minimum 55{\%}), and validity did not vary as a function of the age group of the model nor that of the raters, indicating that the pictures are equally appropriate for use across the entire age range. Strengths and limitations of the DEFFS are discussed.

The Swiss avalanche bulletin is produced twice a day in four languages. Due to the lack of time available for manual translation, a fully automated translation system is employed, based on a catalogue of predefined phrases and predetermined rules of how these phrases can be combined to produce sentences. Because this catalogue of phrases is limited to a small sublanguage, the system is able to automatically translate such sentences from German into the target languages French, Italian and English without subsequent proofreading or correction. Having been operational for two winter seasons, we assess here the quality of the produced texts based on two different surveys where participants rated texts from real avalanche bulletins from both origins, the catalogue of phrases versus manually written and translated texts. With a mean recognition rate of 55 %, users can hardly distinguish between the two types of texts, and give very similar ratings with respect to their language quality. Overall, the output from the catalogue system can be considered virtually equivalent to a text written by avalanche forecasters and then manually translated by professional translators. Furthermore, forecasters declared that all relevant situations were captured by the system with sufficient accuracy. Forecaster’s working load did not change with the introduction of the catalogue: the extra time to find matching sentences is compensated by the fact that they no longer need to double-check manually translated texts. The reduction of daily translation costs is expected to offset the initial development costs within a few years.

Sentiment analysis allows the semantic evaluation of pieces of text according to the expressed sentiments and opinions. While considerable attention has been given to the polarity (positive, negative) of English words, only few studies were interested in the conveyed emotions (joy, anger, surprise, sadness, etc.) especially in other languages. In this paper, we present the elaboration and the evaluation of a new French lexicon considering both polarity and emotion. The elaboration method is based on the semi-automatic translation and expansion to synonyms of the English NRC Word Emotion Association Lexicon (NRC-EmoLex). First, online translators have been automatically queried in order to create a first version of our new French Expanded Emotion Lexicon (FEEL). Then, a human professional translator manually validated the automatically obtained entries and the associated emotions. She agreed with more than 94 % of the pre-validated entries (those found by a majority of translators) and less than 18 % of the remaining entries (those found by very few translators). This result highlights that online tools can be used to get high quality resources with low cost. Annotating a subset of terms by three different annotators shows that the associated sentiments and emotions are consistent. Finally, extensive experiments have been conducted to compare the final version of FEEL with other existing French lexicons. Various French benchmarks for polarity and emotion classifications have been used in these evaluations. Experiments have shown that FEEL obtains competitive results for polarity, and significantly better results for basic emotions.

Databases containing lexical properties on any given orthography are crucial for psycholinguistic research. In the last ten years, a number of lexical databases have been developed for Greek. However, these lack important part-of-speech information. Furthermore, the need for alternative procedures for calculating syllabic measurements and stress information, as well as combination of several metrics to investigate linguistic properties of the Greek language are highlighted. To address these issues, we present a new extensive lexical database of Modern Greek (GreekLex 2) with part-of-speech information for each word and accurate syllabification and orthographic information predictive of stress, as well as several measurements of word similarity and phonetic information. The addition of detailed statistical information about Greek part-of-speech, syllabification, and stress neighbourhood allowed novel analyses of stress distribution within different grammatical categories and syllabic lengths to be carried out. Results showed that the statistical preponderance of stress position on the pre-final syllable that is reported for Greek language is dependent upon grammatical category. Additionally, analyses showed that a proportion higher than 90{\%} of the tokens in the database would be stressed correctly solely by relying on stress neighbourhood information. The database and the scripts for orthographic and phonological syllabification as well as phonetic transcription are available at http://www.psychology.nottingham.ac.uk/greeklex/.

Differences between norm ratings collected when participants are asked to consider more than one picture characteristic are contrasted with the traditional methodological approaches of collecting ratings separately for image constructs. We present data that suggest that reporting normative data, based on methodological procedures that ask participants to consider multiple image constructs simultaneously, could potentially confounded norm data. We provide data for two new image constructs, beauty and the extent to which participants encountered the stimuli in their everyday lives. Analysis of this data suggests that familiarity and encounter are tapping different image constructs. The extent to which an observer encounters an object predicts human judgments of visual complexity. Encountering an image was also found to be an important predictor of beauty, but familiarity with that image was not. Taken together, these results suggest that continuing to collect complexity measures from human judgments is a pointless exercise. Automated measures are more reliable and valid measures, which are demonstrated here as predicting human preferences.

The two main theoretical accounts of the human affective space are the dimensional perspective and the discrete-emotion approach. In recent years, several affective norms have been developed from a dimensional perspective, including ratings for valence and arousal. In contrast, the number of published datasets relying on the discrete-emotion approach is much lower. There is a need to fill this gap, considering that discrete emotions have an effect on word processing above and beyond those of valence and arousal. In the present study, we present ratings from 1,380 participants for a set of 2,266 Spanish words in five discrete emotion categories: happiness, anger, fear, disgust, and sadness. This will be the largest dataset published to date containing ratings for discrete emotions. We also present, for the first time, a fine-grained analysis of the distribution of words into the five emotion categories. This analysis reveals that happiness words are the most consistently related to a single, discrete emotion category. In contrast, there is a tendency for many negative words to belong to more than one discrete emotion. The only exception is disgust words, which overlap least with the other negative emotions. Normative valence and arousal data already exist for all of the words included in this corpus. Thus, the present database will allow researchers to design studies to contrast the predictions of the two most influential theoretical perspectives in this field. These studies will undoubtedly contribute to a deeper understanding of the effects of emotion on word processing.

This paper describes the development of a multilingual and multigenre manually annotated speech dataset, freely available to the research community as ground truth for the evaluation of automatic transcription systems and spoken language translation systems. The dataset includes two video genres—television broadcast news and talk-shows—and covers Flemish, English, German, and Italian, for a total of about 35 h of television speech. Besides segmentation and orthographic transcription, we added a very rich annotation on the audio signal, both at the linguistic level (e.g. filled pauses, pronunciation errors, disfluencies, speech in a foreign language) and at the acoustic level (e.g. background noise and different types of non-speech events). Furthermore, a subset of the transcriptions is translated in four directions, namely Flemish to English, German to English, German to Italian and English to Italian. The development of this dataset was organized in several phases, relying on expert transcribers as well as involving non-expert contributors through crowdsourcing. We first conducted a feasibility study to test and compare two methods for crowdsourcing speech transcription on broadcast news data. These methods are based on different transcription processes (i.e. parallel vs. iterative) and incorporate two different quality control mechanisms. With both methods, we achieved near-expert transcription quality—in terms of word error rate—for English, German and Italian data. Instead, for Flemish data we were not able to get a sufficient response from the crowd to complete the offered transcription tasks. The results obtained demonstrate that the viability of methods for crowdsourcing speech transcription significantly depends on the target language. This paper provides a detailed comparison of the results obtained with the two crowdsourcing methods tested, describes the main characteristics of the final ground truth resource created as well as the methodology adopted, and the guidelines prepared for its development.

The Algerian linguistic situation is very intricate due to the ethnic, geographical and colonial occupation influences which have lead to a complex sociolinguistic environment. As a result of the contact between different languages and accents, the Algerian speech community has acquired a distinctive sociolinguistic situation. In addition to the intra- and inter- lingual variations describing day-to-day linguistic behavior of the Algerian speakers, their speech is characterized by the presence of many linguistic phenomena such as bilingualism and code switching. The study of automatic regional accent recognition in such a type of environment is a new idea in the field of automatic languages, dialect and accent recognition especially that previous studies were conducted using monolingual evaluation data. The assessment of the effectiveness of GMM-UBM and i-vectors frameworks for accent recognition approaches through the use of the Algerian Modern Colloquial Arabic Speech Corpus (AMCASC), which is a linguistic resource collected for this purpose, shows that not only the recording conditions mismatch, channels mismatch, recordings length mismatch and the amplitude clipping which have a non-desirable effect on the effectiveness of these acoustic approaches but also language contact phenomena are other perturbation sources which should be taken into consideration especially in real life applications .

Text reuse is the act of borrowing text from existing documents to create new texts. Freely available and easily accessible large online repositories are not only making reuse of text more common in society but also harder to detect. A major hindrance in the development and evaluation of existing/new mono-lingual text reuse detection methods, especially for South Asian languages, is the unavailability of standardized benchmark corpora. Amongst other things, a gold standard corpus enables researchers to directly compare existing state-of-the-art methods. In our study, we address this gap by developing a benchmark corpus for one of the widely spoken but under resourced languages i.e. Urdu. The COrpus of Urdu News TExt Reuse (COUNTER) corpus contains 1200 documents with real examples of text reuse from the field of journalism. It has been manually annotated at document level with three levels of reuse: wholly derived, partially derived and non derived. We also apply a number of similarity estimation methods on our corpus to show how it can be used for the development, evaluation and comparison of text reuse detection systems for the Urdu language. The corpus is a vital resource for the development and evaluation of text reuse detection systems in general and specifically for Urdu language.

Synesthesia is a neurological phenomenon in which certain types of stimuli elicit involuntary perceptions in an unrelated pathway. A common type of synesthesia is grapheme–color synesthesia, in which the visual perception of letters and numbers stimulates the perception of a specific color. Previous studies have often collected relatively small numbers of grapheme–color associations per synesthete, but the accumulation of a large quantity of data has greater promise for uncovering the mechanisms underlying synesthetic association. In this study, we therefore collected large samples of data from a total of eight synesthetes. All told, we obtained over 1000 synesthetic colors associated with Japanese kanji characters from each of two synesthetes, over 100 synesthetic colors form each of three synesthetes, and about 80 synesthetic colors associated with Japanese hiragana, Latin letters, and Arabic numerals from each of three synesthetes. We then compiled the data into a database, called the KANJI-Synesthetic Colors Database (K-SCD), which has a total of 5122 colors for 483, 46, and 46 Japanese kanji, hiragana, and katakana characters, respectively, as well as for 26 Latin letters and ten Arabic numerals. In addition to introducing the K-SCD, this article demonstrates the database's merits by using two examples, in which two new rules for synesthetic association, “shape similarity” and “synesthetic color clustering,” were found. The K-SCD is publicly accessible (www.cv.jinkan.kyoto-u.ac.jp/site/uploads/K-SCD.xlsm) and will be a valuable resource for those who wish to conduct statistical analyses using a rich dataset in order to uncover the rules governing synesthetic association and to understand its mechanisms.

This paper introduces a multi-layer corpus architecture with multiple tokenizations using the open source historical, diachronic corpus of German called Register in Diachronic German Science. The corpus contains herbal texts printed between the fifteenth and nineteenth centuries and is concerned with the development of a German scientific register, independent of Latin. We will discuss difficulties of transcribing, normalizing and annotating historical texts and will thereby argue for the advantages of multiple layers and multiple tokenizations. A virtually infinite number of annotations can be added to the corpus, without the need for deciding between or discarding interpretations. Thus, this flexible architecture enables multiple normalizations and types of annotation and is open to a wide range of research questions in the humanities. We provide case studies concerning the exploitation of our different normalizations as well as structural, register-specific and linguistic annotations. The corpus architecture allows for its reuse as a resource for corpus-based research approaches.

Building on the success of the VU Amsterdam Metaphor Corpus, which comprises English texts annotated with metaphor following the Metaphor Identification Procedure Vrjie Universiteit (MIPVU; Steen et al. in Cogn Linguist 21(4):765–796, 2010a; Steen et al. in A method for linguistic metaphor identification: from MIP to MIPVU. John Benjamins, Amsterdam/Philadelphia, 2010b), this study has three aims: (1) to adapt and evaluate the transferability and reliability of MIPVU for Mandarin Chinese; (2) to construct a corpus of Chinese texts annotated for metaphor using the adapted procedure; and (3) to examine the distribution of metaphor-related words across Chinese texts in three different written registers: academic discourse, fiction, and news. The results of our inter-annotator reliability test show that MIPVU can be reliably applied to linguistic metaphor identification in Chinese texts. Our metaphor-annotated corpus consists of texts randomly sampled from the Lancaster Corpus of Mandarin Chinese, totaling 30,012 words (about 10,000 for each register). Data analysis reveals that approximately one out of every nine lexical units in our Chinese corpus is related to metaphor, that there is considerable variation in metaphor density across different registers and lexical categories, and that metaphor density is significantly lower in Chinese than in English texts. Our assessment of the replicability of MIPVU for Mandarin Chinese adds to the groundbreaking methodological contribution that Steen et al. (2010a, b) has made to metaphor research. The metaphor-annotated corpus of Mandarin Chinese contributes a valuable language resource for Chinese metaphor researchers, and our analysis of the distribution of metaphor-related words in the corpus offers useful new insights into the extent and use of metaphor in Chinese discourse.

The Princeton WordNet® (PWN) is a widely used lexical knowledge database for semantic information processing. There are now many wordnets under creation for languages worldwide. In this paper, we endeavor to construct a wordnet for Pre-Qin ancient Chinese (PQAC), called PQAC WordNet (PQAC-WN), to process the semantic information of PQAC. In previous work, most recently constructed wordnets have been established either manually by experts or automatically using resources from which translation pairs between English and the target language can be extracted. The former method, however, is time-consuming, and the latter method, owing to a lack of language resources, cannot be performed on PQAC. As a result, a method based on word definitions in a monolingual dictionary is proposed. Specifically, for each sense, kernel words are first extracted from its definition, and the senses of each kernel word are then determined by graph-based Word Sense Disambiguation. Finally, one optimal sense is chosen from the kernel word senses to guide the mapping between the word sense and PWN synset. In this research, we obtain 66 % PQAC senses that can be shared with English and another 14 % language-specific senses that were added to PQAC-WN as new synsets. Overall, the automatic mapping achieves a precision of over 85 %.

Silent-letter endings are often claimed to be a major source of inconsistency in the French orthography. In this report, we introduce Silex, a database designed to facilitate the study of spelling performance in general, and silent-letter endings in particular. It was derived from two large and recent corpora based on child- and adult-targeted material. Silex consists of three kinds of Excel workbooks: a set of Stimuli Selector workbooks that allow researchers to select words based on a variety of statistics and word characteristics; a Table Generator workbook that allows researchers to build consistency distribution tables by selecting specific phonological or orthographic units; and a Master File workbook, from which all statistics were derived, and that allows researchers to compute other statistics. Silex is different from existing databases in the manner that silent-letter endings were coded and how consistency indices were computed. Importantly, Silex provides unconditional- and conditional-consistency indices for silent-letter endings. To demonstrate the utility of Silex, we first described the silent-letter phenomenon in French. We found that, at minimum, 28 {\%} of French words end with a silent letter. Moreover, silent-letter endings are usually t, e, s, x, or d, and the occurrence of these letters is conditioned by the phonological ending of words. Second, we showed how Silex could prove useful for the development of theoretical models and for empirical studies. The novel information provided in Silex as well as the flexibility of this database should enable researchers to advance our understanding of developing and skilled spelling performance.

Most experimental research making use of the Japanese language has involved the 1945 officially standardized kanji (Japanese logographic characters) in the Jōyō kanji list (originally announced by the Japanese government in 1981). However, this list was extensively modified in 2010: five kanji were removed and 196 kanji were added; the latest revision of the list now has a total of 2136 kanji. Using an up-to-date corpus consisting of 11 years' worth of articles printed in the Mainichi Newspaper (2000-2010), we have constructed two novel databases that can be used in psychological research using the Japanese language: (1) a database containing a wide variety of properties on the latest 2136 Jōyō kanji, and (2) a novel database containing 27,950 two-kanji compound words (or jukugo). Based on these two databases, we have created an interactive website ( www.kanjidatabase.com ) to retrieve and store linguistic information to be used in psychological and linguistic experiments. The present paper reports the most important characteristics for the new databases, as well as their value for experimental psychological and linguistic research

Written symbols such as letters have been extensively used in cognitive psychology, be it to understand their contribution to written word recognition or to examine processes involved in other mental functions. Sometimes, however, researchers want to manipulate letters while removing their associated characteristics. A powerful solution to do so is to use new characters, devised to be highly similar to letters, but without associated sound or name. Given the growing use of artificial characters in experimental paradigms, the aim of the present study was to make available the Brussels Artificial Character Sets (BACS), two full, strictly controlled, and portable sets of artificial characters for a broad range of experimental situations.

WordNet, a large lexical database of English, was conceived as a model of human semantic organization. Evidence from timing experiments, association norms, and distributional properties of words supported a semantic network model in which words are interlinked via a small number of lexical and conceptual relations. Its large coverage and unique structure, which allows automatic systems to detect and quantify semantic relatedness among words, soon made WordNet an invaluable tool for natural language processing tasks. Information retrieval, document summarization, and machine translation crucially require word sense discrimination and disambiguation. Wordnets have been built in dozens of languages and for specific technical sublanguages, and the number of applications in research, language technology and pedagogy has grown. Although WordNet’s central focus has shifted from its psycholinguistic origins, its design, based on theories about the structure of the human mental lexicon, is validated as a sound approach to representing the meanings of words. (PsycINFO Database Record (c) 2019 APA, all rights reserved)

Perceptual information is important for the meaning of nouns. We present modality exclusivity norms for 485 Dutch nouns rated on visual, auditory, haptic, gustatory, and olfactory associations. We found these nouns are highly multimodal. They were rated most dominant in vision, and least in olfaction. A factor analysis identified two main dimensions: one loaded strongly on olfaction and gustation (reflecting joint involvement in flavor), and a second loaded strongly on vision and touch (reflecting joint involvement in manipulable objects). In a second study, we validated the ratings with similarity judgments. As expected, words from the same dominant modality were rated more similar than words from different dominant modalities; but – more importantly – this effect was enhanced when word pairs had high modality strength ratings. We further demonstrated the utility of our ratings by investigating whether perceptual modalities are differentially experienced in space, in a third study. Nouns were categorized into their dominant modality and used in a lexical decision experiment where the spatial position of words was either in proximal or distal space. We found words dominant in olfaction were processed faster in proximal than distal space compared to the other modalities, suggesting olfactory information is mentally simulated as “close” to the body. Finally, we collected ratings of emotion (valence, dominance, and arousal) to assess its role in perceptual space simulation, but the valence did not explain the data. So, words are processed differently depending on their perceptual associations, and strength of association is captured by modality exclusivity ratings.

In languages where the position of lexical stress within a word is not predictable from print, readers rely on distributional information extracted from the lexicon in order to assign stress. Lexical databases are thus especially important for researchers willing to address stress assignment in those languages. Here we present Q2Stress, a new database aimed to fill the lack of such a resource for Italian. Q2Stress includes multiple cues readers may use in assigning stress, such as type and token frequency of stress patterns as well as their distribution with respect to number of syllables, grammatical category, word beginnings, word endings, and consonant-vowel structures. Furthermore, for the first time, data for both adults and children are available. Q2Stress may help researchers to answer empirical as well as theoretical questions about stress assignment and stress-related issues, and more in general, to explore the orthography-to-phonology relation in reading. Q2Stress is designed as a user-friendly resource, as it comes with scripts allowing researchers to explore and select their own stimuli according to several criteria as well as summary tables for overall data analysis.

textcopyright} 2016 Psychonomic Society, Inc.Using a megastudy approach, we developed a database of lexical variables and lexical decision reaction times and accuracy rates for more than 25,000 traditional Chinese two-character compound words. Each word was responded to by about 33 native Cantonese speakers in Hong Kong. This resource provides a valuable adjunct to influential mega-databases, such as the Chinese single-character, English, French, and Dutch Lexicon Projects. Three analyses were conducted to illustrate the potential uses of the database. First, we compared the proportion of variance in lexical decision performance accounted for by six word frequency measures and established that the best predictor was Cai and Brysbaert's (PLoS One, 5, e10729, 2010) contextual diversity subtitle frequency. Second, we ran virtual replications of three previously published lexical decision experiments and found convergence between the original experiments and the present megastudy. Finally, we conducted item-level regression analyses to examine the effects of theoretically important lexical variables in our normative data. This is the first publicly available large-scale repository of behavioral responses pertaining to Chinese two-character compound word processing, which should be of substantial interest to psychologists, linguists, and other researchers.

Sublexical phonotactic regularities in language have a major impact on language development, as well as on speech processing and production throughout the entire lifespan. To understand the impact of phonotactic regularities on speech and language functions at the behavioral and neural levels, it is essential to have access to oral language corpora to study these complex phenomena in different languages. Yet, probably because of their complexity, oral language corpora remain less common than written language corpora. This article presents the first corpus and database of spoken Quebec French syllables and phones: SyllabO+. This corpus contains phonetic transcriptions of over 300,000 syllables (over 690,000 phones) extracted from recordings of 184 healthy adult native Quebec French speakers, ranging in age from 20 to 97 years. To ensure the representativeness of the corpus, these recordings were made in both formal and familiar communication contexts. Phonotactic distributional statistics (e.g., syllable and co-occurrence frequencies, percentages, percentile ranks, transition probabilities, and pointwise mutual information) were computed from the corpus. An open-access online application to search the database was developed, and is available at www.speechneurolab.ca/syllabo . In this article, we present a brief overview of the corpus, as well as the syllable and phone databases, and we discuss their practical applications in various fields of research, including cognitive neuroscience, psycholinguistics, neurolinguistics, experimental psychology, phonetics, and phonology. Nonacademic practical applications are also discussed, including uses in speech-language pathology.

With the Developmental Lexicon Project (DeveL), we present a large-scale study that was conducted to collect data on visual word recognition in German across the lifespan. A total of 800 children from Grades 1 to 6, as well as two groups of younger and older adults, participated in the study and completed a lexical decision and a naming task. We provide a database for 1,152 German words, comprising behavioral data from seven different stages of reading development, along with sublexical and lexical characteristics for all stimuli. The present article describes our motivation for this project, explains the methods we used to collect the data, and reports analyses on the reliability of our results. In addition, we explored developmental changes in three marker effects in psycholinguistic research: word length, word frequency, and orthographic similarity. The database is available online.

Semantic feature norms (e.g., STIMULUS: car → RESPONSE: <has four wheels>) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic features are typically collected in experimental settings and then manually annotated by the researchers into feature types (e.g., perceptual features, taxonomic features, etc.) by means of content analyses—that is, by using taxonomies of feature types and having independent coders perform the annotation task. However, the ways in which such content analyses are typically performed and reported are not consistent across the literature. This constitutes a serious methodological problem that might undermine the theoretical claims based on such annotations. In this study, we first offer a review of some of the released datasets of annotated semantic feature norms and the related taxonomies used for content analysis. We then provide theoretical and methodological insights in relation to the content analysis methodology. Finally, we apply content analysis to a new dataset of semantic features and show how the method should be applied in order to deliver reliable annotations and replicable coding schemes. We tackle the following issues: (1) taxonomy structure, (2) the description of categories, (3) coder training, and (4) sustainability of the coding scheme—that is, comparison of the annotations provided by trained versus novice coders. The outcomes of the project are threefold: We provide methodological guidelines for semantic feature classification; we provide a revised and adapted taxonomy that can (arguably) be applied to both concrete and abstract concepts; and we provide a dataset of annotated semantic feature norms.

As human activity and interaction increasingly take place online, the digital residues of these activities provide a valuable window into a range of psychological and social processes. A great deal of progress has been made toward utilizing these opportunities; however, the complexity of managing and analyzing the quantities of data currently available has limited both the types of analysis used and the number of researchers able to make use of these data. Although fields such as computer science have developed a range of techniques and methods for handling these difficulties, making use of those tools has often required specialized knowledge and programming experience. The Text Analysis, Crawling, and Interpretation Tool (TACIT) is designed to bridge this gap by providing an intuitive tool and interface for making use of state-of-the-art methods in text analysis and large-scale data management. Furthermore, TACIT is implemented as an open, extensible, plugin-driven architecture, which will allow other researchers to extend and expand these capabilities as new methods become available.

The main purpose of this study was to report age-based subjective age-of-acquisition (AoA) norms for 600 Turkish words. A total of 115 children, 100 young adults, 115 middle-aged adults, and 127 older adults provided AoA estimates for 600 words on a 7-point scale. The intraclass correlations suggested high reliability, and the AoA estimates were highly correlated across the four age groups. Children gave earlier AoA estimates than the three adult groups; this was true for high-frequency as well as low-frequency words. In addition to the means and standard deviations of the AoA estimates, we report word frequency, concreteness, and imageability ratings, as well as word length measures (numbers of syllables and letters), for the 600 words as supplemental materials. The present ratings represent a potentially useful database for researchers working on lexical processing as well as other aspects of cognitive processing, such as autobiographical memory.

This article presents K-SPAN (Korean Surface Phonetics and Neighborhoods), a database of surface phonetic forms and several measures of phonological neighborhood density for 63,836 Korean words. Currently publicly available Korean corpora are limited by the fact that they only provide orthographic representations in Hangeul, which is problematic since phonetic forms in Korean cannot be reliably predicted from orthographic forms. We describe the method used to derive the surface phonetic forms from a publicly available orthographic corpus of Korean, and report on several statistics calculated using this database; namely, segment unigram frequencies, which are compared to previously reported results, along with segment-based and syllable-based neighborhood density statistics for three types of representation: an "orthographic" form, which is a quasi-phonological representation, a "conservative" form, which maintains all known contrasts, and a "modern" form, which represents the pronunciation of contemporary Seoul Korean. These representations are rendered in an ASCII-encoded scheme, which allows users to query the corpus without having to read Korean orthography, and permits the calculation of a wide range of phonological measures.

Emotionally charged pictorial materials are frequently used in phobia research, but no existing standardized picture database is dedicated to the study of different phobias. The present work describes the results of two independent studies through which we sought to develop and validate this type of database-a Set of Fear Inducing Pictures (SFIP). In Study 1, 270 fear-relevant and 130 neutral stimuli were rated for fear, arousal, and valence by four groups of participants; small-animal (N = 34), blood/injection (N = 26), social-fearful (N = 35), and nonfearful participants (N = 22). The results from Study 1 were employed to develop the final version of the SFIP, which includes fear-relevant images of social exposure (N = 40), blood/injection (N = 80), spiders/bugs (N = 80), and angry faces (N = 30), as well as 726 neutral photographs. In Study 2, we aimed to validate the SFIP in a sample of spider, blood/injection, social-fearful, and control individuals (N = 66). The fear-relevant images were rated as being more unpleasant and led to greater fear and arousal in fearful than in nonfearful individuals. The fear images differentiated between the three fear groups in the expected directions. Overall, the present findings provide evidence for the high validity of the SFIP and confirm that the set may be successfully used in phobia research.

This article presents subjective rating norms for a new set of Stills And Videos of facial Expressions-the SAVE database. Twenty nonprofessional models were filmed while posing in three different facial expressions (smile, neutral, and frown). After each pose, the models completed the PANAS questionnaire, and reported more positive affect after smiling and more negative affect after frowning. From the shooting material, stills and 5 s and 10 s videos were edited (total stimulus set = 180). A different sample of 120 participants evaluated the stimuli for attractiveness, arousal, clarity, genuineness, familiarity, intensity, valence, and similarity. Overall, facial expression had a main effect in all of the evaluated dimensions, with smiling models obtaining the highest ratings. Frowning expressions were perceived as being more arousing, clearer, and more intense, but also as more negative than neutral expressions. Stimulus presentation format only influenced the ratings of attractiveness, familiarity, genuineness, and intensity. The attractiveness and familiarity ratings increased with longer exposure times, whereas genuineness decreased. The ratings in the several dimensions were correlated. The subjective norms of facial stimuli presented in this article have potential applications to the work of researchers in several research domains. From our database, researchers may choose the most adequate stimulus presentation format for a particular experiment, select and manipulate the dimensions of interest, and control for the remaining dimensions. The full stimulus set and descriptive results (means, standard deviations, and confidence intervals) for each stimulus per dimension are provided as supplementary material.

Emotions are highly influential to many psychological processes. Indeed, research employing emotional stimuli is rapidly escalating across the field of psychology. However, challenges remain regarding discrete evocation of frequently co-elicited emotions such as amusement and happiness, or anger and disgust. Further, as much contemporary work in emotion employs college students, we sought to additionally evaluate the efficacy of film clips to discretely elicit these more challenging emotions in a young adult population using an online medium. The internet is an important tool for investigating responses to emotional stimuli, but validations of emotionally evocative film clips across laboratory and web-based settings are limited in the literature. An additional obstacle is identifying stimuli amidst the numerous film clip validation studies. During our investigation, we recognized the lack of a categorical database to facilitate rapid identification of useful film clips for individual researchers' unique investigations. Consequently, here we also sought to produce the first compilation of such stimuli into an accessible and comprehensive catalog. We based our catalog upon prior work as well as our own, and identified 24 articles and 295 film clips from four decades of research. We present information on the validation of these clips in addition to our own research validating six clips using online administration settings. The results of our search in the literature and our own study are presented in tables designed to facilitate and improve a selection of highly valid film stimuli for future research.

The Affective Norms for Polish Short Texts (ANPST) dataset (Imbir, 2016d) is a list of 718 affective sentence stimuli with known affective properties with respect to subjectively perceived valence, arousal, dominance, origin, subjective significance, and source. This article examines the reliability of the ANPST and the impact of population type and sex on affective ratings. The ANPST dataset was introduced to provide a recognized method of eliciting affective states with linguistic stimuli more complex than single words and that included contextual information and thus are less ambiguous in interpretation than single word. Analysis of the properties of the ANPST dataset showed that norms collected are reliable in terms of split-half estimation and that the distributions of ratings are similar to those obtained in other affective norms studies. The pattern of correlations was the same as that found in analysis of an affective norms dataset for words based on the same six variables. Female psychology students' valence ratings were also more polarized than those of their female student peers studying other subjects, but arousal ratings were only higher for negative words. Differences also appeared for all other measured dimensions. Women's valence ratings were found to be more polarized and arousal ratings were higher than those made by men, and differences were also present for dominance, origin, and subjective significance. The ANPST is the first Polish language list of sentence stimuli and could easily be adapted for other languages and cultures.

FANchild (French Affective Norms for Children) provides norms of valence and arousal for a large corpus of French words (N = 720) rated by 908 French children and adolescents (ages 7, 9, 11, and 13). The ratings were made using the Self-Assessment Manikin (Lang, 1980). Because it combines evaluations of arousal and valence and includes ratings provided by 7-, 9-, 11-, and 13-year-olds, this database complements and extends existing French-language databases. Good response reliability was observed in each of the four age groups. Despite a significant level of consensus, we found age differences in both the valence and arousal ratings: Seven- and 9-year-old children gave higher mean valence and arousal ratings than did the other age groups. Moreover, the tendency to judge words positively (i.e., positive bias) decreased with age. This age- and sex-related database will enable French-speaking researchers to study how the emotional character of words influences their cognitive processing, and how this influence evolves with age. FANchild is available at https://www.researchgate.net/profile/Catherine{\_}Monnier/contributions.

Psycholinguistic research has been advanced by the development of word recognition megastudies. For instance, the English Lexicon Project (Balota et al., 2007) provides researchers with access to naming and lexical-decision latencies for over 40,000 words. In the present work, we extended the megastudy approach to a task that emphasizes semantic processing. Using a concrete/abstract semantic decision (i.e., does the word refer to something concrete or abstract?), we collected decision latencies and accuracy rates for 10,000 English words. The stimuli were concrete and abstract words selected from Brysbaert, Warriner, and Kuperman's (2013) comprehensive list of concreteness ratings. In total, 321 participants provided responses to 1,000 words each. Whereas semantic effects tend to be quite modest in naming and lexical decision studies, analyses of the concrete/abstract semantic decision responses show that a substantial proportion of variance can be explained by semantic variables. The item-level and trial-level data will be useful for other researchers interested in the semantic processing of concrete and abstract words

ASL-LEX is a lexical database that catalogues information about nearly 1,000 signs in American Sign Language (ASL). It includes the following information: subjective frequency ratings from 25–31 deaf signers, iconicity ratings from 21–37 hearing non-signers, videoclip duration, sign length (onset and offset), grammatical class, and whether the sign is initialized, a fingerspelled loan sign, or a compound. Information about English translations is available for a subset of signs (e.g., alternate translations, translation consistency). In addition, phonological properties (sign type, selected fingers, flexion, major and minor location, and movement) were coded and used to generate sub-lexical frequency and neighborhood density estimates. ASL-LEX is intended for use by researchers, educators, and students who are interested in the properties of the ASL lexicon. An interactive website where the database can be browsed and downloaded is available at http://asl-lex.org.

The use of the corpus becomes essential in the development of applications based on natural language processing (NLP). In Ecuador, these applications are incompatible because in each region use words outside the context of Spanish. This article presents the development of a corpus compatible with Ecuadorian natural language words. We applied a identification algorithm to take advantage of local literature and power a new data base. The corpus mounted is verified by a quantitative and qualitative comparison with an open access corpus. The result is the first corpus in this country with high scalability and great versatility. {\textcopyright} 2017 IEEE.

For most of the Uralic languages, there is a lack of systematically collected, consequently transcribed and morphologically annotated text corpora. This paper sums up the steps, the preliminary results and the future directions of building a linguistic corpus of some Uralic languages, namely Tundra Nenets, Udmurt, Synya Khanty, and Surgut Khanty. The experiences of building a corpus containing both old and modern, and written and oral data samples are discussed. Principles concerning data collection strategies of languages with different level of vitality and endangerment are discussed. Methodologies and challenges of data processing, and the levels of linguistic annotation are also described in detail.

The Remote Associates Test (RAT) has been used to measure creativity, however few repositories or standardizations of test items exist, like the normative data on 144 items provided by Bowden and Jung-Beeman. comRAT is a computational solver which has been used to solve the compound RAT in linguistic and visual forms, showing correlation to human performance over the normative data provided by Bowden and Jung-Beeman. This paper describes using a variant of comRAT, comRAT-G, to generate and construct a repository of compound RAT items for use in the cognitive psychology and cognitive modelling community. Around 17 million compound Remote Associates Test items are created from nouns alone, aiming to provide control over (i) frequency of occurrence of query items, (ii) answer items, (iii) the probability of coming up with an answer, (iv) keeping one or more query items constant and (v) keeping the answer constant. Queries produced by comRAT-G are evaluated in a study in comparison with queries from the normative dataset of Bowden and Jung-Beeman, showing that comRAT-G queries are similar to the established query set.

Words are widely used as stimuli in cognitive research. Because of their complexity, using words requires strict control of their objective (lexical and sublexical) and subjective properties. In this work, we present the Minho Word Pool (MWP), a dataset that provides normative values of imageability, concreteness, and subjective frequency for 3,800 (European) Portuguese words-three subjective measures that, in spite of being used extensively in research, have been scarce for Portuguese. Data were collected with 2,357 college students who were native speakers of European Portuguese. The participants rated 100 words drawn randomly from the full set for each of the three subjective indices, using a Web survey procedure (via a URL link). Analyses comparing the MWP ratings with those obtained for the same words from other national and international databases showed that the MWP norms are reliable and valid, thus providing researchers with a useful tool to support research in all neuroscientific areas using verbal stimuli. The MWP norms can be downloaded along with this article or from http://p-pal.di.uminho.pt/about/databases .

An idiom is classically defined as a formulaic sequence whose meaning is comprised of more than the sum of its parts. For this reason, idioms pose a unique problem for models of sentence processing, as researchers must take into account how idioms vary and along what dimensions, as these factors can modulate the ease with which an idiomatic interpretation can be activated. In order to help ensure external validity and comparability across studies, idiom research benefits from the availability of publicly available resources reporting ratings from a large number of native speakers. Resources such as the one outlined in the current paper facilitate opportunities for consensus across studies on idiom processing and help to further our goals as a research community. To this end, descriptive norms were obtained for 870 American English idioms from 2,100 participants along five dimensions: familiarity, meaningfulness, literal plausibility, global decomposability, and predictability. Idiom familiarity and meaningfulness strongly correlated with one another, whereas familiarity and meaningfulness were positively correlated with both global decomposability and predictability. Correlations with previous norming studies are also discussed.

In the present study, we collected valence, arousal, concreteness, familiarity, imageability, and context availabili-ty ratings for a total of 1,100 Chinese words. The ratings for all variables were collected with 9-point Likert scales. We tested the reliability of the present database by comparing it to the extant Chinese Affective Word System, and performed split-half correlations for all six variables. We then evaluated the relationships between all variables. Regarding the affective variables, we found a typical quadratic relation between va-lence and arousal, in line with previous findings. Likewise, significant correlations were found between the semantic var-iables. Importantly, we explored the relationships between ratings for the affective variables (i.e., valence and arousal) and concreteness ratings, suggesting that valence and arousal ratings can predict concreteness ratings. This database of af-fective norms will be a valuable source of information for emotion research that makes use of Chinese words, and will enable researchers to use highly controlled Chinese verbal stimuli to more reliably investigate the relation between cog-nition and emotion.

textcopyright} 2016, The Author(s). In this article, we introduce HelexKids, an online written-word database for Greek-speaking children in primary education (Grades 1 to 6). The database is organized on a grade-by-grade basis, and on a cumulative basis by combining Grade 1 with Grades 2 to 6. It provides values for Zipf, frequency per million, dispersion, estimated word frequency per million, standard word frequency, contextual diversity, orthographic Levenshtein distance, and lemma frequency. These values are derived from 116 textbooks used in primary education in Greece and Cyprus, producing a total of 68,692 different word types. HelexKids was developed to assist researchers in studying language development, educators in selecting age-appropriate items for teaching, as well as writers and authors of educational books for Greek/Cypriot children. The database is open access and can be searched online at www.helexkids.org.

This study presents a normative database of Spanish restricted length word stems that provides useful information for the selection of stimuli in memory experiments with Word Stem Completion (WSC) tasks. The database includes indices relative to stems (total baseline completion, priming baseline completion, priming, number of completions, ratio between given and deleted letters, and syllabic structure), and indices relative to characteristics of the words used to obtain the stems (frequency, familiarity, number of meanings, length, number of syllables, arousal, and valence). A WSC task was performed by 515 participants to calculate priming and baseline indices. An Exploratory Factor Analysis showed that these indices are grouped in four factors: perceptual, lexical, emotional, and response competition. Stepwise regression analyses performed with these factors showed that the lexical, response competition, and perceptual factors predict priming baseline completion, while only the lexical factor predicts priming. The model that best explains the relationship between priming and priming baseline completion was a cubic model, and the optimum baseline values for achieving priming were between .31 and .36. These norms can be downloaded as Supplemental Materials for this article from https://nuvol.uv.es/owncloud/index.php/s/hpj9by1qbENdjfj .

Words that have been learned early in life are responded to faster than words that have been acquired later. Subjective ratings of acquisition ages have been successfully employed to study the effect of age of acquisition (AoA). Although a large number of norms exist in many languages, fewer are available for German. Therefore, subjective AoA ratings for 3,259 German words were collected online, includ-ing 2,363 nouns and 473 verbs. These words were presented in lists of 140 words, and participants rated the age in years at which they had first learned each word. A split-half correla-tion testified to a high internal reliability. There were also high correlations with rated AoA values for subsets of the items that had been collected in previous studies, in both German and English. Age and gender were found to influence the ratings very weakly, in that older and male participants tended to give slightly higher age ratings. Education, multilingualism, and frequent usage of languages other than German did not exert an influence on the rating values. These new ratings will ex-tend the currently existing norms available for language and reading research across languages and will provide re-searchers with a wider choice of word stimuli. The ratings are available expressed in two measurements: age in years, and AoA rated on a 7-point Likert scale.

? 2016 Psychonomic Society, Inc.Semantic feature production norms provide many quantitative measures of different feature and concept variables that are necessary to solve some debates surrounding the nature of the organization, both normal and pathological, of semantic memory. Despite the current existence of norms for different languages, there are still no published norms in Spanish. This article presents a new set of norms collected from 810 participants for 400 living and nonliving concepts among Spanish speakers. These norms consist of empirical collections of features that participants used to describe the concepts. Four files were elaborated: a concept?feature file, a concept?concept matrix, a feature?feature matrix, and a significantly correlated features file. We expect that these norms will be useful for researchers in the fields of experimental psychology, neuropsychology, and psycholinguistics.

We present Chinese translation norms for 1429 English words. Chinese-English bilinguals (N=28) were asked to provide the first Chinese translation that came to mind for 1429 English words. The results revealed that 71{\%} of the English words received more than one correct translation indicating the large amount of translation ambiguity when translating from English to Chinese. The relationship between translation ambiguity and word frequency, concreteness and language proficiency was investigated. Although the significant correlations were not strong, results revealed that English word frequency was positively correlated with the number of alternative translations, whereas English word concreteness was negatively correlated with the number of translations. Importantly, regression analyses showed that the number of Chinese translations was predicted by word frequency and concreteness. Furthermore, an interaction between these predictors revealed that the number of translations was more affected by word frequency for more concrete words than for less concrete words. In addition, mixed-effects modelling showed that word frequency, concreteness and English language proficiency were all significant predictors of whether or not a dominant translation was provided. Finally, correlations between the word frequencies of English words and their Chinese dominant translations were higher for translation-unambiguous pairs than for translation-ambiguous pairs. The translation norms are made available in a database together with lexical information about the words, which will be a useful resource for researchers investigating Chinese-English bilingual language processing.

This article presents the Danish NOMCO Corpus, an annotated multimodal collection of video-recorded first acquaintance conversations between Danish speakers. The annotation includes speech transcription including word boundaries, and formal as well as functional coding of gestural behaviours, specifically head movements, facial expressions, and body posture. The corpus has served as the empirical basis for a number of studies of communication phenomena related to turn management, feedback exchange, information packaging and the expression of emotional attitudes. We describe the annotation scheme, procedure, and annotation results. We then summarise a number of studies conducted on the corpus. The corpus is available for research and teaching purposes through the authors of this article.

We present a collection of parallel treebanks that have been automatically aligned on both the terminal and the non-terminal constituent level for use in syntax-based machine translation. We describe how they were constructed and applied to a syntax- and example-based machine translation system called Parse and Corpus-Based Machine Translation (PaCo-MT). For the language pair Dutch to English, we present non-terminal alignment evaluation scores for a variety of tree alignment approaches. Finally, based on the parallel treebanks created by these approaches, we evaluate the MT system itself and compare the scores with those of Moses, a current state-of-the-art statistical MT system, when trained on the same data.

To simplify the problem of studying how people learn natural language, researchers use the artificial grammar learning (AGL) task. In this task, participants study letter strings constructed according to the rules of an artificial grammar and subsequently attempt to discriminate grammatical from ungrammatical test strings. Although the data from these experiments are usually analyzed by comparing the mean discrimination performance between experimental conditions, this practice discards information about the individual items and participants that could otherwise help uncover the particular features of strings associated with grammaticality judgments. However, feature analysis is tedious to compute, often complicated, and ill-defined in the literature. Moreover, the data violate the assumption of independence underlying standard linear regression models, leading to Type I error inflation. To solve these problems, we present AGSuite, a free Shiny application for researchers studying AGL. The suite's intuitive Web-based user interface allows researchers to generate strings from a database of published grammars, compute feature measures (e.g., Levenshtein distance) for each letter string, and conduct a feature analysis on the strings using linear mixed effects (LME) analyses. The LME analysis solves the inflation of Type I errors that afflicts more common methods of repeated measures regression analysis. Finally, the software can generate a number of graphical representations of the data to support an accurate interpretation of results. We hope the ease and availability of these tools will encourage researchers to take full advantage of item-level variance in their datasets in the study of AGL. We moreover discuss the broader applicability of the tools for researchers looking to conduct feature analysis in any field.

Words are frequently used as stimuli in cognitive psychology experiments, for example, in recognition memory studies. In these experiments, it is often desirable to control for the words' psycholinguistic properties because differences in such properties across experi-mental conditions might introduce undesirable confounds. In order to avoid confounds, stud-ies typically check to see if various affective and lexico-semantic properties are matched across experimental conditions, and so databases that contain values for these properties are needed. While word ratings for these variables exist in English and other European lan-guages, ratings for Chinese words are not comprehensive. In particular, while ratings for sin-gle characters exist, ratings for two-character words—which often have different meanings than their constituent characters, are scarce. In this study, ratings for 292 two-character Chi-nese nouns were obtained from Cantonese speakers in Hong Kong. Affective variables, including valence and arousal, and lexico-semantic variables, including familiarity, concrete-ness, and imageability, were rated in the study. The words were selected from a film subtitle database containing word frequency information that could be extracted and listed along-side the resulting ratings. Overall, the subjective ratings showed good reliability across all rated dimensions, as well as good reliability within and between the different groups of par-ticipants who each rated a subset of the words. Moreover, several well-established relation-ships between the variables found consistently in other languages were also observed in this study, demonstrating that the ratings are valid. The resulting word database can be used in studies where control for the above psycholinguistic variables is critical to the research design. PLOS ONE | https://doi.org/10.1371/journal.pone.0174569 March 27, 2017 1 / 16 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Yee LTS (2017) Valence, arousal, familiarity, concreteness, and imageability ratings for 292 two-character Chinese nouns in Cantonese speakers in Hong Kong. PLoS ONE 12(3):

Picture databases are commonly used in experimental work on various aspects of emotion processing. However, existing standardized facial databases, typically used to explore emotion recognition, can be augmented with more contextual information for studying emotion and social perception. Moreover, the perception of social engagement, i.e., the degree of interaction or engagement inferred between the people in target pictures, has not been measured. In this paper, we describe the development of a database comprising 203 black-and-white line drawings depicting people within various situational contexts, and normed on perceived emotional valence, intensity, and social engagement, a new construct. Analyses of ratings collected from 62 young adults (30 females, 32 males; mean age 22 years) revealed the typical quadratic relationship between valence and intensity, i.e., stimuli that are more emotionally charged, whether positively or negatively valenced, are more intense than emotionally-neutral stimuli. Moreover, the results showed significant linear and quadratic relationships between valence and social engagement ratings, indicating that emotionally-charged social scenes were perceived as more engaging than emotionally-neutral social scenes. This new database will facilitate investigations of how people perceive and interpret social and emotional information in everyday interactions, and is offered as a resource to experimenters involved in social and/or emotional processing research.

Words are considered semantically ambiguous if they have more than one meaning and can be used in multiple contexts. A number of recent studies have provided objective ambiguity measures by using a corpus-based approach and have demonstrated ambiguity advantages in both naming and lexical decision tasks. Although the predictive power of objective ambiguity measures has been examined in several alphabetic language systems, the effects in logographic languages remain unclear. Moreover, most ambiguity measures do not explicitly address how the various contexts associated with a given word relate to each other. To explore these issues, we computed the contextual diversity (Adelman, Brown, {\&} Quesada, Psychological Science, 17; 814-823, 2006) and semantic ambiguity (Hoffman, Lambon Ralph, {\&} Rogers, Behavior Research Methods, 45; 718-730, 2013) of traditional Chinese single-character words based on the Academia Sinica Balanced Corpus, where contextual diversity was used to evaluate the present semantic space. We then derived a novel ambiguity measure, namely semantic variability, by computing the distance properties of the distinct clusters grouped by the contexts that contained a given word. We demonstrated that semantic variability was superior to semantic diversity in accounting for the variance in naming response times, suggesting that considering the substructure of the various contexts associated with a given word can provide a relatively fine scale of ambiguity information for a word. All of the context and ambiguity measures for 2,418 Chinese single-character words are provided as supplementary materials.

Corpora are essential resources for language studies, as well as for training statistical natural language processing systems. Although very large English corpora have been built, only relatively small corpora are available for many varieties of English. National top-level domains (e.g., .au, .ca) could be exploited to automatically build web corpora, but it is unclear whether such corpora would reflect the corresponding national varieties of English; i.e., would a web corpus built from the .ca domain correspond to Canadian English? In this article we build web corpora from national top-level domains corresponding to countries in which English is widely spoken. We then carry out statistical analyses of these corpora in terms of keywords, measures of corpus comparison based on the Chi-square test and spelling variants, and the frequencies of words known to be marked in particular varieties of English. We find evidence that the web corpora indeed reflect the corresponding national varieties of English. We then demonstrate, through a case study on the analysis of Canadianisms, that these corpora could be valuable lexicographical resources.

The extent to which processing words involves breaking them down into smaller units or morphemes or is the result of an interactive activation of other units, such as meanings, letters, and sounds (e.g., dis-agree-ment vs. disagreement), is currently under debate. Disentangling morphology from phonology and semantics is often a methodological challenge, because orthogonal manipulations are difficult to achieve (e.g., semantically unrelated words are often phonologically related: casual-casualty and, vice versa, sign-signal). The present norms provide a morphological classification of 3,263 suffixed derived words from two widely spoken languages: English (2,204 words) and Spanish (1,059 words). Morphologically complex words were sorted into four categories according to the nature of their relationship with the base word: phonologically transparent (friend-friendly), phonologically opaque (child-children), semantically transparent (habit-habitual), and semantically opaque (event-eventual). In addition, ratings were gathered for age of acquisition, imageability, and semantic distance (i.e., the extent to which the meaning of the complex derived form could be drawn from the meaning of its base constituents). The norms were completed by adding values for word frequency; word length in number of phonemes, letters, and syllables; lexical similarity, as measured by the number of neighbors; and morphological family size. A series of comparative analyses from the collated ratings for the base and derived words were also carried out. The results are discussed in relation to recent findings.

The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning.

We present a new set of subjective age-of-acquisition (AoA) ratings for 299 words (158 nouns, 141 verbs) in 25 languages from five language families (Afro-Asiatic: Semitic languages; Altaic: one Turkic language: Indo-European: Baltic, Celtic, Germanic, Hellenic, Slavic, and Romance languages; Niger-Congo: one Bantu language; Uralic: Finnic and Ugric languages). Adult native speakers reported the age at which they had learned each word. We present a comparison of the AoA ratings across all languages by contrasting them in pairs. This comparison shows a consistency in the orders of ratings across the 25 languages. The data were then analyzed (1) to ascertain how the demographic characteristics of the participants influenced AoA estimations and (2) to assess differences caused by the exact form of the target question (when did you learn vs. when do children learn this word); (3) to compare the ratings obtained in our study to those of previous studies; and (4) to assess the validity of our study by comparison with quasi-objective AoA norms derived from the MacArthur-Bates Communicative Development Inventories (MB-CDI). All 299 words were judged as being acquired early (mostly before the age of 6 years). AoA ratings were associated with the raters' social or language status, but not with the raters' age or education. Parents reported words as being learned earlier, and bilinguals reported learning them later. Estimations of the age at which children learn the words revealed significantly lower ratings of AoA. Finally, comparisons with previous AoA and MB-CDI norms support the validity of the present estimations. Our AoA ratings are available for research or other purposes.

Evidence based medicine (EBM) urges the medical doctor to incorporate the latest available clinical evidence at point of care. A major stumbling block in the practice of EBM is the difficulty to keep up to date with the clinical advances. In this paper we describe a corpus designed for the development and testing of text processing tools for EBM, in particular for tasks related to the extraction and summarisation of answers and corresponding evidence related to a clinical query. The corpus is based on material from the Clinical Inquiries section of The Journal of Family Practice. It was gathered and annotated by a combination of automated information extraction, crowdsourcing tasks, and manual annotation. It has been used for the original summarisation task for which it was designed, as well as for other related tasks such as the appraisal of clinical evidence and the clustering of the results. The corpus is available at SourceForge (http://sourceforge.net/projects/ebmsumcorpus/).

Lexical frequency is one of the strongest predictors of word processing time. The frequencies are often calculated from book-based corpora, or more recently from subtitle-based corpora. We present new frequencies based on Twitter, blog posts, or newspapers for 66 languages. We show that these frequencies predict lexical decision reaction times similar to the already existing frequencies, or even better than them. These new frequencies are freely available and may be downloaded from http://worldlex.lexique.org .

Color has the ability to influence a variety of human behaviors, such as object recognition, the identification of facial expressions, and the ability to categorize stimuli as positive or negative. Researchers have started to examine the relationship between emotional words and colors, and the findings have revealed that brightness is often associated with positive emotional words and darkness with negative emotional words (e.g., Meier, Robinson, {\&} Clore, Psychological Science, 15, 82-87, 2004). In addition, words such as anger and failure seem to be inherently associated with the color red (e.g., Kuhbandner {\&} Pekrun). The purpose of the present study was to construct norms for positive and negative emotion and emotion-laden words and their color associations. Participants were asked to provide the first color that came to mind for a set of 160 emotional items. The results revealed that the color RED was most commonly associated with negative emotion and emotion-laden words, whereas YELLOW and WHITE were associated with positive emotion and emotion-laden words, respectively. The present work provides researchers with a large database to aid in stimulus construction and selection.

A corpus of Polish speech, which has been collected for the purpose of automatic speech recognition (ASR) and text-to-speech (TTS) systems applications, is presented. The corpus consists of several groups of recordings: read sentences, spoken commands, a phonetically balanced TTS training corpus, telephonic speech and others. In summary duration of recordings is above 25 h. Number of unique speakers amounts to 166. The majority of them being in an age group of 20–35 and one third of them being female. Analysis of unique word occurrence frequency in relation to larger text resources has been concluded. From them, most commonly appearing words have been found and presented. The corpus was used as training data for the ASR system. Results of cross-validation training and testing the SARMATA ASR system using our corpus have shown that phrase recognition rate is 91.9 %. The corpus was additionally evaluated in comparative test against the CORPORA corpus, which had shown major increase in phrase recognition rate in favour of our corpus.

Textual analysis has been applied to various fields, such as discourse analysis, corpus studies, text leveling, and automated essay evaluation. Several tools have been devel-oped for analyzing texts written in alphabetic languages such as English and Spanish. However, currently there is no tool available for analyzing Chinese-language texts. This article introduces a tool for the automated analysis of simplified and traditional Chinese texts, called the Chinese Readability Index Explorer (CRIE). Composed of four subsystems and incorporating 82 multilevel linguistic features, CRIE is able to conduct the major tasks of segmentation, syntactic parsing, and feature extraction. Furthermore, the integration of linguis-tic features with machine learning models enables CRIE to provide leveling and diagnostic information for texts in lan-guage arts, texts for learning Chinese as a foreign language, and texts with domain knowledge. The usage and validation of the functions provided by CRIE are also introduced.

Many studies have shown that how words are processed in a variety of language-related tasks is affected by their age of acquisition (AoA). Most AoA norms have been collected for nouns, a fact that limits the extent to which verb stimuli can be adequately manipulated and controlled in empirical studies. With the aim of increasing the number of verbs with AoA values in Spanish, 900 college students were recruited to provide subjective estimates for a total of 4,640 infinitive and reflexive forms. An AoA score for each verb was obtained by averaging the responses of the participants, and these norms were included, together with additional quantitative information (standard deviations, ranges, and z scores), in a database that can be downloaded with this article as supplemental materials.

Our understanding of the mental lexicon, the way meaning is extracted from word forms, is almost entirely built on data from spoken languages. While there is much work demonstrating that in many ways the linguistic structure and psychological mechanisms for processing signed language and spoken language processing are the same, less is known about the signed language mental lexicon. In this dissertation, I examine the structure of the American Sign Language mental lexicon, and the ways meaning can be extracted from the manual/visual signal. In the third chapter of this dissertation I ask whether a single cognitive architecture might explain diverse behavioral patterns in signed and spoken language. Chen and Mirman (2012) presented a computational model of word processing that unified opposite effects of neighborhood density in speech production, perception, and written word recognition. Carreiras et al. (2008) demonstrate that neighborhood density effects in Spanish Sign Language (LSE) also vary depending on whether the neighbors share the same handshape or location. We present a spreading activation architecture that borrows the principles proposed by Chen and Mirman (2012), and show that if this architecture is elaborated to incorporate relatively minor facts about either 1) the time course of sign perception or 2) the frequency of sub-lexical units in sign languages, it produces data that match the experimental findings from sign languages. This work serves as a proof of concept that a single cognitive architecture could underlie both sign and word recognition. In the second chapter I present ASL-LEX, a lexical database for ASL that catalogues more than forty properties about almost 1,000 signs. The database includes, for example, information about each sign's iconicity, phonological make-up, and neighborhood density. I use this information to better understand the structure of the ASL lexicon, the distribution of each of these properties, and the relationships between these properties. This lexical database is the largest and most comprehensive database of ASL, and can be used by researchers to develop experiments and by educators to identify and support vocabulary development. In the fourth chapter, I use ASL-LEX to develop a tightly-controlled study of sign perception. I ask whether neighborhood density and sub-lexical frequency play a role in sign perception, and if the mechanisms of sign perception are affected by early language experience. Eighty deaf participants with varying early language backgrounds completed a lexical decision task. I find that neighborhood density inhibits sign perception in people with low early ASL exposure, but has no effect in people with high early ASL exposure. Location frequency inhibits sign perception in all people, but the effect is stronger in people with low early ASL exposure. This suggests that impoverished access to ASL early in life has lasting consequences for sign perception. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

The design of experimental tasks in psychology and linguistics requires using stimulus with properties and characteristics in standardized values. This allows predicting with higher accuracy the impact of the stimulus presentation. The lexical associative norms are instruments that determine the strength of association between two concepts. The most common method to construct these norms is to take a free response from a presentation of a cue word. The main goal of this study was to construct lexical associative norms of 407 Spanish words. 800 students from Ciudad de Córdoba, Argentina, participated in the study. Quantitative analyses were performed taking into account the number of valid answers, blank and non valid answers, and number of associates per item. A qualitative classification was performed according to the strength of association. Additionally, it is presented a group of psycholinguistic indexes for a better description of the items used. Correlation analysis demonstrated a strong and negative relation between the frequency of first and second associations and the number of associations per item. This study pretends to be highly useful in research in psychology and linguistic where it is required consulting the norms presented to the design of evaluation instruments. (PsycINFO Database Record (c) 2018 APA, all rights reserved)

In many research domains, researchers have employed gradually morphing pictures to study perception under ambiguity. Despite their inherent utility, only a limited number of stimulus sets are available, and those sets vary substantially in quality and perceptual complexity. Here we present normative data for 40 morphing picture series. In all sets, line drawings of pictures of common objects are morphed over 15 iterations into a completely different object. Objects are either morphed from an animate to an inanimate object (or vice versa) or morphed within the animate and inanimate object categories. These pictures, together with the normative naming data presented here, will be of value for research on a diverse range of questions, from perceptual processing to decision making.

textcopyright} 2015, Psychonomic Society, Inc. Relative meaning frequency is a critical factor to consider in studies of semantic ambiguity. In this work, we examined how this measure may change across the European and Rioplatense dialects of Spanish, as well as how the overall distributional properties differ between Spanish and English, using a computer-assisted norming approach based on dictionary definitions (Armstrong, Tokowicz, {\&} Plaut, 2012). The results showed that the two dialects differ considerably in terms of the relative meaning frequencies of their constituent homonyms, and that the overall distributions of relative frequencies vary considerably across languages, as well. These results highlight the need for localized norms to design powerful studies of semantic ambiguity and suggest that dialectal differences may be responsible for some discrepant effects related to homonymy. In quantifying the reliability of the norms, we also established that as few as seven ratings are needed to converge on a highly stable set of ratings. This approach is therefore a very practical means of acquiring essential data in studies of semantic ambiguity, relative to past approaches, such as those based on the classification of free associates. The norms also present new possibilities for studying semantic ambiguity effects within and between populations who speak one or more languages. The norms and associated software are available for download at http://edom.cnbc.cmu.edu/ or http://www.bcbl.eu/databases/edom/.

Faces impart exhaustive information about their bearers, and are widely used as stimuli in psychological research. Yet many extant facial stimulus sets have sub- stantially less detail than faces encountered in real life. In this paper, we describe a new database of facial stimuli, the Multi-Racial Mega-Resolution database (MR2). The MR2 includes 74 extremely high resolution images of European, African, and East Asian faces. This database provides a high-quality, diverse, naturalistic, and well-controlled facial image set for use in research. The MR2 is available under a Creative Commons license, and may be accessed online.

Humans appear to rely on spatial mappings to describe and represent concepts. In particular, conceptual cueing refers to the effect whereby after reading or hearing a particular word, the location of observers' visual attention in space can be systematically shifted in a particular direction. For example, words such as "sun" and "happy" orient attention upwards, whereas words such as "basement" and "bitter" orient attention downwards. This area of research has garnered much interest, particularly within the embodied cognition framework, for its potential to enhance our understanding of the interaction between abstract cognitive processes such as language and basic visual processes such as attention and stimulus processing. To date, however, this area has relied on subjective classification criteria to determine whether words ought to be classified as having a meaning that implies "up" or "down." The present study, therefore, provides a set of 498 items that have each been systematically rated by over 90 participants, providing refined, continuous measures of the extent to which people associate given words with particular spatial dimensions. The resulting database provides an objective means to aid item-selection for future research in this area.

Normative databases for pictorial stimuli are widely used in research on language processing in order to control for a number of psycholinguistic variables in the selected stimuli. Such resources are lacking for Arabic and its dialectal varieties. In the present study, we aimed to provide Tunisian Arabic (TA) normative data for 348 line drawings taken from Cycowicz, Friedman, Rothstein, and Snodgrass (1997), which include Snodgrass and Vanderwart's (1980) 260 pictures. Norms were collected for the following psycholinguistic variables: name agreement, familiarity, subjective frequency, and imageability. Word length data (in numbers of phonemes and syllables) are also listed in the database. We investigated the effects of these variables on word reading in TA. We found that word length and frequency were the best predictors of word-reading latencies in TA. Name agreement was also a significant predictor of word-reading latencies. A particularly interesting finding was that the semantic variables, imageability and familiarity, affected word-reading latencies in TA. Thus, it would seem that TA readers rely on semantics even when reading individual Arabic words that are transparent in terms of orthography-to-phonology mappings. This database represents a precious and much-needed psycholinguistic resource for researchers investigating language processing in Arabic-speaking populations.

In the speeded word fragment completion task, participants have to complete fragments such as tom{\_}to as quickly and accurately as possible. Previous work has shown that this paradigm can successfully capture subtle priming effects (Heyman, De Deyne, Hutchison, {\&} Storms Behavior Research Methods, 47, 580-606, 2015). In addition, it has several advantages over the widely used lexical decision task. That is, the speeded word fragment completion task is more efficient, more engaging, and easier. Given its potential, we conducted a study to gather speeded word fragment completion norms. The goal of this megastudy was twofold. On the one hand, it provides a rich database of over 8,000 stimuli, which can, for instance, be used in future research to equate stimuli on baseline response times. On the other hand, the aim was to gain insight into the underlying processes of the speeded word fragment completion task. To this end, item-level regression and mixed-effects analyses were performed on the response latencies using 23 predictor variables. Since all items were selected from the Dutch Lexicon Project (Keuleers, Diependaele, {\&} Brysbaert Frontiers in Psychology, 1, 174, 2010), we ran the same analyses on lexical decision latencies to compare the two tasks. Overall, the results revealed many similarities, but also some remarkable differences, which are discussed. We propose that both tasks are complementary when examining visual word recognition. The article ends with a discussion of potential process models of the speeded word fragment completion task.

In the present study, we introduce affective norms for a new set of Spanish words, the Madrid Affective Database for Spanish (MADS), that were scored on two emotional dimensions (valence and arousal) and on five discrete emotional categories (happiness, anger, sadness, fear, and disgust), as well as on concreteness, by 660 Spanish native speakers. Measures of several objective psycholinguistic variables—grammatical class, word frequency, number of letters, and number of syllables—for the words are also included. We observed high split-half reliabilities for every emotional variable and a strong quadratic relationship between valence and arousal. Additional analyses revealed several associations between the affective dimensions and discrete emotions, as well as with some psycholinguistic variables. This new corpus complements and extends prior databases in Spanish and allows for designing new experiments investigating the influence of affective content in language processing under both dimensional and discrete theoretical conceptions of emotion. These norms can be downloaded as supplemental materials for this article from www.dropbox.com/s/o6dpw3irk6utfhy/Hinojosa{\%}20et{\%}20al{\_}Supplementary{\%}20materials.xlsx?dl=0.

Houses have often been used as comparison stimuli in face-processing studies because of the many attributes they share with faces (e.g., distinct members of a basic category, consistent internal features, mono-orientation, and relative familiarity). Despite this, no large, well-controlled databases of photographs of houses that have been developed for research use currently exist. To address this gap, we photographed 100 houses and carefully edited these images. We then asked 41 undergraduate students (18 to 31 years of age) to rate each house on three dimensions: typicality, likeability, and face-likeness. The ratings had a high degree of face validity, and analyses revealed a significant positive correlation between typicality and likeability. We anticipate that this stimulus set (i.e., the DalHouses) and the associated ratings will prove useful to face-processing researchers by minimizing the effort required to acquire stimuli and allowing for easier replication and extension of studies. The photographs of all 100 houses and their ratings data can be obtained at http://dx.doi.org/10.6084/m9.figshare.1279430.

The Nencki Affective Picture System (NAPS; Marchewka, {\.{Z}}urawski, Jednor{\'{o}}g, {\&} Grabowska, Behavior Research Methods, 2014) is a standardized set of 1,356 realistic, high-quality photographs divided into five categories (people, faces, animals, objects, and landscapes). NAPS has been primarily standardized along the affective dimensions of valence, arousal, and approach-avoidance, yet the characteristics of discrete emotions expressed by the images have not been investigated thus far. The aim of the present study was to collect normative ratings according to categorical models of emotions. A subset of 510 images from the original NAPS set was selected in order to proportionally cover the whole dimensional affective space. Among these, using three available classification methods, we identified images eliciting distinguishable discrete emotions. We introduce the basic-emotion normative ratings for the Nencki Affective Picture System (NAPS BE), which will allow researchers to control and manipulate stimulus properties specifically for their experimental questions of interest. The NAPS BE system is freely accessible to the scientific community for noncommercial use as supplementary materials to this article.

The most important forms of idioms in Chinese, chengyus (CYs), have a fixed length of four Chinese characters. Most CYs are joined structures of two, two-character words—subject–verb units (SVs), verb–object units (VOs), structures of modification (SMs), or verb–verb units—or of four, one-character words. Both the first and second pairs of words in a four-word CY form an SV, a VO, or an SM. In the present study, normative measures were obtained for knowledge, familiarity, subjective frequency, age of acquisition, predictability, literality, and compositionality for 350 CYs, and the influences of the CYs' syntactic structures on the descriptive norms were analyzed. Consistent with previous studies, all of the norms yielded a high reliability, and there were strong correlations between knowledge, familiarity, subjective frequency, and age of acquisition, and between familiarity and predictability. Unlike in previous studies (e.g., Libben {\&} Titone in Memory {\&} Cognition, 36, 1103–1121, 2008), however, we observed a strong correlation between literality and compositionality. In general, the results seem to support a hybrid view of idiom representation and comprehension. According to the evaluation scores, we further concluded that CYs consisting of just one SM are less likely to be decomposable than those with a VOVO composition, and also less likely to be recognized through their constituent words, or to be familiar to, known by, or encountered by users. CYs with an SMSM composition are less likely than VOVO CYs to be decomposable or to be known or encountered by users. Experimental studies should investigate how a CY's syntactic structure influences its representation and comprehension.

Recently, genre collection and automatic genre identification for the web has attracted much attention. However, currently there is no genre-annotated corpus of web pages where inter-annotator reliability has been established, i.e. the corpora are either not tested for inter-annotator reliability or exhibit low inter-coder agreement. Annotation has also mostly been carried out by a small number of experts, leading to concerns with regard to scalability of these annotation efforts and transferability of the schemes to annotators outside these small expert groups. In this paper, we tackle these problems by using crowd-sourcing for genre annotation, leading to the Leeds Web Genre Corpus—the first web corpus which is, demonstrably reliably annotated for genre and which can be easily and cost-effectively expanded using naive annotators. We also show that the corpus is source and topic diverse.

Knowledge of thematic relations is an area of increased interest in semantic memory research because it is crucial to many cognitive processes. One methodological issue that researchers face is how to identify pairs of thematically related concepts that are well-established in semantic memory for most people. In this article, we review existing methods of assessing thematic relatedness and provide thematic relatedness production norming data for 100 object concepts. In addition, 1,174 related concept pairs obtained from the production norms were classified as reflecting one of the five subtypes of relations: attributive, argument, coordinate, locative, and temporal. The database and methodology will be useful for researchers interested in the effects of thematic knowledge on language processing, analogical reasoning, similarity judgments, and memory. These data will also benefit researchers interested in investigating potential processing differences among the five types of semantic relations.

This article presents subjective rating norms for a new set of 600 symbols, depicting various contents (e.g., transportation, technology, and leisure activities) that can be used by researchers in different fields. Symbols were evaluated for aesthetic appeal, familiarity, visual complexity, concreteness, valence, arousal, and meaningfulness. The normative data were obtained from 388 participants, and no gender differences were found. Descriptive results (means, standard deviations, and confidence intervals) for each symbol in each dimension are presented. Overall, the dimensions were highly correlated. Additionally, participants were asked to briefly describe the meaning of each symbol. The results indicate that the present symbol set is varied, allowing for the selection of exemplars with different levels on the seven examined dimensions. This set of symbols constitutes a tool with potential for research in different areas. The database with all of the symbols is available as supplemental materials.

ABSTRACTIn brain and behaviour, gustation, and olfaction are closely linked to emotional processing. This paper shows that similarly, words associated with taste and smell, such as “pungent” and “delicious”, are on average more emotionally valenced than words associated with the other senses, such as “beige” (visual) and “echoing” (auditory). Moreover, taste and smell words occur more frequently in emotionally valenced phrases, for example, “fragrant” modifies more emotionally valenced nouns (“fragrant kiss”) than the visual adjective “yellow” (“yellow house”). It is argued that taste and smell words form an affectively loaded part of the English lexicon. Taste and smell words are also shown to be more emotionally flexible in that words such as “sweet” can be combined with both good and bad nouns (“sweet delight” versus “sweet disaster”), much more so than is the case for sensory words for the other modalities. The paper discusses implications for theories of embodied language understanding.

Change blindness has been a topic of interest in cognitive sciences for decades. Change detection experiments are frequently used for studying various research topics such as attention and perception. However, creating change detection stimuli is tedious and there is no open repository of such stimuli using natural scenes. We introduce the Change Blindness (CB) Database with object changes in 130 colored images of natural indoor scenes. The size and eccentricity are provided for all the changes as well as reaction time data from a baseline experiment. In addition, we have two specialized satellite databases that are subsets of the 130 images. In one set, changes are seen in rooms or in mirrors in those rooms (Mirror Change Database). In the other, changes occur in a room or out a window (Window Change Database). Both the sets have controlled background, change size, and eccentricity. The CB Database is intended to provide researchers with a stimulus set of natural scenes with defined stimulus parameters that can be used for a wide range of experiments. The CB Database can be found at http://search.bwh.harvard.edu/new/CBDatabase.html .

Research in metaphor processing has made extensive use of the normed metaphor database created by Katz, Paivio, Marschark, {\&} Clark (Metaphor and Symbolic Activity, 3, 191–214, 1988). Because of the plasticity of figurative language, we conducted a renorming of selected metaphors from the database on a new student population. Correlations between Katz et al.'s and the present data showed that the pattern of responses has remained highly consistent across time and populations. The consistency of the normative ratings allows us to be confident in future research that will use the Katz et al. collection.

The EU-Emotion Stimulus Set is a newly developed collection of dynamic multimodal emotion and mental state representations. A total of 20 emotions and mental states are represented through facial expressions, vocal expressions, body gestures and contextual social scenes. This emotion set is portrayed by a multi-ethnic group of child and adult actors. Here we present the validation results, as well as participant ratings of the emotional valence, arousal and intensity of the visual stimuli from this emotion stimulus set. The EU-Emotion Stimulus Set is available for use by the scientific community and the validation data are provided as a supplement available for download.

Studies of semantic variables (e.g., concreteness) and affective variables (i.e., valence and arousal) have traditionally tended to run in different directions. However, in recent years there has been growing interest in studying the relationship, as well as the potential overlaps, between the two. This article describes a database that provides subjective ratings for 1,400 Spanish words for valence, arousal, concreteness, imageability, context availability, and familiarity. Data were collected online through a process involving 826 university students. The results showed a high interrater reliability for all of the variables examined, as well as high correlations between our affective and semantic values and norms currently available in other Spanish databases. Regarding the affective variables, the typical quadratic correlation between valence and arousal ratings was obtained. Likewise, significant correlations were found between the lexico-semantic variables. Importantly, we obtained moderate negative correlations between emotionality and both concreteness and imageability. This is in line with the claim that abstract words have more affective associations than concrete ones (Kousta, Vigliocco, Vinson, Andrews, {\&} Del Campo, 2011). The present Spanish database is suitable for experimental research into the effects of both affective properties and lexico-semantic variables on word processing and memory.

Many experimental research designs require images of novel objects. Here we introduce the Novel Object and Unusual Name (NOUN) Database. This database contains 64 primary novel object images and additional novel exemplars for ten basic- and nine global-level object categories. The objects' novelty was confirmed by both self-report and a lack of consensus on questions that required participants to name and identify the objects. We also found that object novelty correlated with qualifying naming responses pertaining to the objects' colors. The results from a similarity sorting task (and a subsequent multidimensional scaling analysis on the similarity ratings) demonstrated that the objects are complex and distinct entities that vary along several featural dimensions beyond simply shape and color. A final experiment confirmed that additional item exemplars comprised both sub- and superordinate categories. These images may be useful in a variety of settings, particularly for developmental psychology and other research in the language, categorization, perception, visual memory, and related domains.

The present study provides norms for Deese/Roediger-McDermott (DRM) lists that were used to create false memories in native speakers of Italian. The word lists reported in this article are based on the DRM lists that have been used extensively to examine illusory memories in English speakers (Deese in Journal of Experimental Psychology, 58, 17-22, 1959; Roediger {\&} McDermott in Journal of Experimental Psychology: Learning, Memory, {\&} Cognition, 21, 803-814, 1995). We translated the 24 critical lures from 24 English DRM lists and created semantically associated Italian word lists that were then normed with native Italian speakers. Overall, the participants recalled 63{\%} of the list items and 22{\%} of the critical lures with the word lists developed. In addition, 56{\%} of the list items and 82{\%} of the critical lures were recognized by the participants. The present study provides a set of Italian lists that can be used by researchers interested in evaluating false memories in Italian-speaking participants.

We present a corpus-based prosodic analysis with the aim of uncovering the relationship between dialogue acts, personality and prosody in view to providing guidelines for the ECA Greta’s text-to-speech system. The corpus used is the SEMAINE corpus, featuring four different personalities, further annotated for dialogue acts and prosodic features. In order to show the importance of the choice of dialogue act taxonomy, two different taxonomies were used, the first corresponding to Searle’s taxonomy of speech acts and the second, inspired by Bunt’s DIT++, including a division of directive acts into finer categories. Our results show that finer-grained distinctions are important when choosing a taxonomy. We also show with some preliminary results that the prosodic correlates of dialogue acts are not always as cited in the literature and prove more complex and variable. By studying the realisation of different directive acts, we also observe differences in the communicative strategies of the ECA depending on personality, in view to providing input to a speech system.

False-memory illusions have been widely studied using the Deese/Roediger-McDermott paradigm (DRM). In this paradigm, words semantically related to a single nonpresented critical word are studied. In a later memory test, critical words are often falsely recalled and recognized. The present normative study was conducted to measure the theme identifiability of 60 associative word lists in Spanish that include six words (e.g., stove, coat, blanket, scarf, chill, and bonnet) that are simultaneously associated with three critical words (e.g., HEAT, COLD, and WINTER; Beato {\&} D{\'{i}}ez, Psicothema, 26, 457-463, 2011). Different levels of backward associative strength were used in the construction of the DRM lists. In addition, we used two types of instructions to obtain theme identifiability. In the without-explanation condition, traditional instructions were used, requesting participants to write the theme list. In the with-explanation condition, the false-memory effect and how the lists were built were explained, and an example of a DRM list and critical words was shown. Participants then had to discover the critical words. The results showed that all lists produced theme identifiability. Moreover, some lists had a higher theme identifiability rate (e.g., 61 {\%} for the critical words LOVE, BOYFRIEND, COUPLE) than others (e.g., 24 {\%} for CITY, PLACE, VILLAGE). After comparing the theme identifiabilities in the different conditions, the results indicated higher theme identifiability when the false-memory effect was explained than without such an explanation. Overall, these new normative data provide a useful tool for those experiments that, for example, aim to analyze the wide differences observed in false memory with DRM lists and the role of theme identifiability.

The LSE-Sign database is a free online tool for selecting Spanish Sign Language stimulus materials to be used in experiments. It contains 2,400 individual signs taken from a recent standardized LSE dictionary, and a further 2,700 related nonsigns. Each entry is coded for a wide range of grammatical, phonological, and articulatory information, including handshape, location, movement, and non-manual elements. The database is accessible via a graphically based search facility which is highly flexible both in terms of the search options available and the way the results are displayed. LSE-Sign is available at the following website: http://www.bcbl.eu/databases/lse/.

This study introduces the Tool for the Automatic Analysis of Cohesion (TAACO), a freely available text analysis tool that is easy to use, works on most operating systems (Windows, Mac, and Linux), is housed on a user's hard drive (rather than having an Internet interface), allows for the batch processing of text files, and incorporates over 150 classic and recently developed indices related to text cohesion. The study validates TAACO by investigating how its indices related to local, global, and overall text cohesion can predict expert judgments of text coherence and essay quality. The findings of this study provide predictive validation of TAACO and support the notion that expert judgments of text coherence and quality are either negatively correlated or not predicted by local and overall text cohesion indices, but are positively predicted by global indices of cohesion. Combined, these findings provide supporting evidence that coherence for expert raters is a property of global cohesion and not of local cohesion, and that expert ratings of text quality are positively related to global cohesion.

Rebus puzzles and compound remote associate problems have been successfully used to study problem solving. These problems are physically compact, often can be solved within short time limits, and have unambiguous solutions, and English versions have been normed for solving rates and levels of difficulty. Many studies on problem solving with sudden insight have taken advantage of these features in paradigms that require many quick solutions (e.g., solution priming, visual hemifield presentations, electroencephalography, fMRI, and eyetracking). In order to promote this vein of research in Italy, as well, we created and tested Italian versions of both of these tests. The data collected across three studies yielded a pool of 88 rebus puzzles and 122 compound remote associate problems within a moderate range of difficulty. This article provides both sets of problems with their normative data, for use in future research.

In the present study, we report naming latencies and norms for 327 photos of objects in Dutch. We provide norms for eight psycholinguistic variables: age of acquisition, familiarity, imageability, image agreement, objective and subjective visual complexity, word frequency, word length in syllables and letters, and name agreement. Furthermore, multiple regression analyses revealed that the significant predictors of photo-naming latencies were name agreement, word frequency, imageability, and image agreement. The naming latencies, norms, and stimuli are provided as supplemental materials.

Despite flourishing research on the relationship between emotion and literal language, and despite the pervasiveness of figurative expressions in communication, the role of figurative language in conveying affect has been underinvestigated. This study provides affective and psycholinguistic norms for 619 German idiomatic expressions and explores the relationships between affective and psycholinguistic idiom properties. German native speakers rated each idiom for emotional valence, arousal, familiarity, semantic transparency, figurativeness, and concreteness. They also described the figurative meaning of each idiom and rated how confident they were about the attributed meaning. The results showed that idioms rated high in valence were also rated high in arousal. Negative idioms were rated as more arousing than positive ones, in line with results from single words. Furthermore, arousal correlated positively with figurativeness (supporting the idea that figurative expressions are more emotionally engaging than literal expressions) and with concreteness and semantic transparency. This suggests that idioms may convey a more direct reference to sensory representations, mediated by the meanings of their constituting words. Arousal correlated positively with familiarity. In addition, positive idioms were rated as more familiar than negative idioms. Finally, idioms without a literal counterpart were rated as more emotionally valenced and arousing than idioms with a literal counterpart. Although the meanings of ambiguous idioms were less correctly defined than those of unambiguous idioms, ambiguous idioms were rated as more concrete than unambiguous ones. We also discuss the relationships between the various psycholinguistic variables characterizing idioms, with reference to the literature on idiom structure and processing.

Automatic syntactic analysis of a corpus requires detailed lexical and morphological information that cannot always be harvested from traditional dictionaries. Therefore the development of a treebank presents an opportunity to simultaneously enrich the lexicon. In building NorGramBank, we use an incremental parsebanking approach, in which a corpus is parsed and disambiguated, and after improvements to the grammar and the lexicon, reparsed. In this context we have implemented a text preprocessing interface where annotators can enter unknown words or missing lexical information either before parsing or during disambiguation. The information added to the lexicon in this way may be of great interest both to lexicographers and to other language technology efforts.

This paper presents a historical Arabic corpus named HAC. At this early embryonic stage of the project, we report about the design, the architecture and some of the experiments which we have conducted on HAC. The corpus, and accordingly the search results, will be represented using a primary XML exchange format. This will serve as an intermediate exchange tool within the project and will allow the user to process the results offline using some external tools. HAC is made up of Classical Arabic texts that cover 1600 years of language use; the Quranic text, Modern Standard Arabic texts, as well as a variety of monolingual Arabic dictionaries. The development of this historical corpus assists linguists and Arabic language learners to effectively explore, understand, and discover interesting knowledge hidden in millions of instances of language use. We used techniques from the field of natural language processing to process the data and a graph-based representation for the corpus. We provided researchers with an export facility to render further linguistic analysis possible.

This article presents the Spanish Iarg-AnCora corpus (400 k-words, 13,883 sentences) annotated with the implicit arguments of deverbal nominalizations (18,397 occurrences). We describe the methodology used to create it, focusing on the annotation scheme and criteria adopted. The corpus was manually annotated and an interannotator agreement test was conducted (81 % observed agreement) in order to ensure the reliability of the final resource. The annotation of implicit arguments results in an important gain in argument and thematic role coverage (128 % on average). It is the first corpus annotated with implicit arguments for the Spanish language with a wide coverage that is freely available. This corpus can subsequently be used by machine learning-based semantic role labeling systems, and for the linguistic analysis of implicit arguments grounded on real data. Semantic analyzers are essential components of current language technology applications, which need to obtain a deeper understanding of the text in order to make inferences at the highest level to obtain qualitative improvements in the results.

The Active Listening Corpus (ALICO) is a multimodal data set of spontaneous dyadic conversations in German with diverse speech and gestural annotations of both dialogue partners. The annotations consist of short feedback expression transcriptions with corresponding communicative function interpretations as well as segmentations of interpausal units, words, rhythmic prominence intervals and vowel-to-vowel intervals. Additionally, ALICO contains head gesture annotations of both interlocutors. The corpus contributes to research on spontaneous human–human interaction, on functional relations between modalities, and timing variability in dialogue. It also provides data that differentiates between distracted and attentive listeners. We describe the main characteristics of the corpus and briefly present the most important results obtained from analyses in recent years.

This chapter discusses free-association responses to the primary purposes and other responses selected from the Palermo–Jenkins norms. A useful supplement to free-association norms is the additional determination of associations to responses that are elicited on the original test. These supplemental norms increase the number of association hierarchies available to the investigator and also provide information concerning the independent probabilities of chains of words. Free-association responses allow the independent manipulation of associative directionality of pairs of words, for example, A and B, that is, it becomes possible to choose word pairs on the basis of either the A-B or B-A associative strength. Listing of the associative probabilities of other words, given in response to B, can greatly increase the size of the pool of associative triads, that is, A-B-C chains. The original norms can be used to discover A-B-C word chains or word pairs varying in degree of bidirectionality. However, such a procedure identifies only a limited number of usable word pairs or chains.

In this study, we aimed to provide a large-scale set of psycholinguistic norms for 3,314 traditional Chinese characters, along with their naming reaction times (RTs), collected from 140 Chinese speakers. The lexical and semantic variables in the database include frequency, regularity, familiarity, consistency, number of strokes, homophone density, semantic ambiguity rating, phonetic combinability, semantic combinability, and the number of disyllabic compound words formed by a character. Multiple regression analyses were conducted to examine the predictive powers of these variables for the naming RTs. The results demonstrated that these variables could account for a significant portion of variance (55.8{\%}) in the naming RTs. An additional multiple regression analysis was conducted to demonstrate the effects of consistency and character frequency. Overall, the regression results were consistent with the findings of previous studies on Chinese character naming. This database should be useful for research into Chinese language processing, Chinese education, or cross-linguistic comparisons. The database can be accessed via an online inquiry system (http://ball.ling.sinica.edu.tw/namingdatabase/index.html).

Improvised acting is a viable technique to study expressive human communication and to shed light into actors’ creativity. The USC CreativeIT database provides a novel, freely-available multimodal resource for the study of theatrical improvisation and rich expressive human behavior (speech and body language) in dyadic interactions. The theoretical design of the database is based on the well-established improvisation technique of Active Analysis in order to provide naturally induced affective and expressive, goal-driven interactions. This database contains dyadic theatrical improvisations performed by 16 actors, providing detailed full body motion capture data and audio data of each participant in an interaction. The carefully engineered data collection, the improvisation design to elicit natural emotions and expressive speech and body language, as well as the well-developed annotation processes provide a gateway to study and model various aspects of theatrical performance, expressive behaviors and human communication and interaction.

This contribution aims to establish a set of validated vocal Italian pseudowords that convey three emotional tones (angry, happy, and neutral) for prosodic emotional processing research. We elaborated the materials by following a series of specific steps. First, we tested the valence of a set of written pseudowords generated by specific software. Two Italian actors (male and female) then recorded the resulting subset of linguistically legal and neutral pseudowords in three emotional tones. Finally, on the basis of the results of independent ratings of emotional intensity, we selected a set of 30 audio stimuli expressed in each of the three different emotions. Acoustic analyses indicated that the prosodic indexes of fundamental frequency, vocal intensity, and speech rate anchored individual perceptions of the emotions expressed. Finally, the acoustic profile of the set of emotional stimuli confirmed previous findings. The happy tone stimuli showed high f0 values, high intensity, high pitch variability, and a faster speech rate. The angry tone stimuli were also characterized by high f0 and intensity, but by relatively smaller pitch variability and a lower speech rate. This last profile echoes the description of "cold anger." This new set of prosodic emotion stimuli will constitute a useful resource for future research that requires emotional prosody materials. It could be used both for Italian and for cross-language studies.

Language identification, as the task of determining the language a given text is written in, has progressed substantially in recent decades. However, three main issues remain still unresolved: (1) distinction of similar languages, (2) detection of multilingualism in a single document, and (3) identifying the language of short texts. In this paper, we describe our work on the development of a benchmark to encourage further research in these three directions, set forth an evaluation framework suitable for the task, and make a dataset of annotated tweets publicly available for research purposes. We also describe the shared task we organized to validate and assess the evaluation framework and dataset with systems submitted by seven different participants, and analyze the performance of these systems. The evaluation of the results submitted by the participants of the shared task helped us shed some light on the shortcomings of state-of-the-art language identification systems, and gives insight into the extent to which the brevity, multilingualism, and language similarity found in texts exacerbate the performance of language identifiers. Our dataset with nearly 35,000 tweets and the evaluation framework provide researchers and practitioners with suitable resources to further study the aforementioned issues on language identification within a common setting that enables to compare results with one another.

The majority of the world’s languages have little to no NLP resources or tools. This is due to a lack of training data (“resources”) over which tools, such as taggers or parsers, can be trained. In recent years, there have been increasing efforts to apply NLP methods to a much broader swath of the world’s languages. In many cases this involves bootstrapping the learning process with enriched or partially enriched resources. We propose that Interlinear Glossed Text (IGT), a very common form of annotated data used in the field of linguistics, has great potential for bootstrapping NLP tools for resource-poor languages. Although IGT is generally very richly annotated, and can be enriched even further (e.g., through structural projection), much of the content is not easily consumable by machines since it remains “trapped” in linguistic scholarly documents and in human readable form. In this paper, we describe the expansion of the ODIN resource—a database containing many thousands of instances of IGT for over a thousand languages. We enrich the original IGT data by adding word alignment and syntactic structure. To make the data in ODIN more readily consumable by tool developers and NLP researchers, we adopt and extend a new XML format for IGT, called Xigt. We also develop two packages for manipulating IGT data: one, INTENT, enriches raw IGT automatically, and the other, XigtEdit, is a graphical IGT editor.

The Chinese Proposition Bank (CPB) is a corpus annotated with semantic roles for the arguments of verbal and nominalized predicates. The semantic roles for the core arguments are defined in a predicate-specific manner. That is, a set of semantic roles, numerically identified, are defined for each sense of a predicate lemma and recorded in a valency lexicon called frame files. The predicate-specific manner in which the semantic roles are defined reduces the cognitive burden on the annotators since they only need to internalize a few roles at a time and this has contributed to the consistency in annotation. It was also a sensible approach given the contentious issue of how many semantic roles are needed if one were to adopt of set of global semantic roles that apply to all predicates. A downside of this approach, however, is that the predicate-specific roles may not be consistent across predicates, and this inconsistency has a negative impact on training automatic systems. Given the progress that has been made in defining semantic roles in the last decade or so, time is ripe for adopting a set of general semantic roles. In this article, we describe our effort to “re-annotate” the CPB with a set of “global” semantic roles that are predicate-independent and investigate their impact on automatic semantic role labeling systems. When defining these global semantic roles, we strive to make them compatible with a recently published ISO standards on the annotation of semantic roles (ISO 24617-4:2014 SemAF-SR) while taking the linguistic characteristics of the Chinese language into account. We show that in spite of the much larger number of global semantic roles, the accuracy of an off-the-shelf semantic role labeling system retrained on the data re-annotated with global semantic roles is comparable to that trained on the data set with the original predicate-specific semantic roles. We also argue that the re-annotated data set, together with the original data, provides the user with more flexibility when using the corpus.

Sentiment analysis aims to extract the sentiment polarity of given segment of text. Polarity resources that indicate the sentiment polarity of words are commonly used in different approaches. While English is the richest language in regard to having such resources, the majority of other languages, including Turkish, lack polarity resources. In this work we present the first comprehensive Turkish polarity resource, SentiTurkNet, where three polarity scores are assigned to each synset in the Turkish WordNet, indicating its positivity, negativity, and objectivity (neutrality) levels. Our method is general and applicable to other languages. Evaluation results for Turkish show that the polarity scores obtained through this method are more accurate compared to those obtained through direct translation (mapping) from SentiWordNet.

This article describes a new software tool called RadicalLocator that can be used to automatically identify (e.g., for visual inspection) individual target radicals (i.e., groups of strokes) in written Chinese characters. We first briefly clarify why this software is useful for research purposes and discuss the factors that make this pattern recognition task so difficult. We then describe how the software can be downloaded and installed, and used to identify the radicals in characters for the purposes of, for example, selecting materials for psycholinguistic experiments. Finally, we discuss several known limitations of the software and heuristics for addressing them.

We collected subjective frequency, age-of-acquisition, and imageability norms for 319 acronyms from French adults. Objective printed frequency, bigram frequency, and lengths in letters, phonemes, and syllables, as well as orthographic neighbors, were computed. The time taken to read acronyms aloud was also recorded. Correlational analyses indicated that the relations between the psycholinguistic variables were similar to those usually found for common words (e.g., highly imageable acronyms were more frequent and learned earlier in life than less imageable acronyms), but were generally weaker in the former than in the latter. Linear mixed-model analyses performed on the reading latencies revealed that the main determinants were the voicing feature of initial phonemes, the type of pronunciation of the acronyms (ambiguous vs. unambiguous, typical vs. atypical characteristics), length (number of letters and number of syllables), together with bigram frequency, printed frequency, and imageability. Both objective frequency and imageability interacted reliably with the ambiguous typical and ambiguous atypical properties. Accuracy was predicted by the number of letters and by imageability factors: More errors occurred on longer than on shorter acronyms, and also more errors on less imageable than on more imageable acronyms. The theoretical and methodological implications of the findings for the understanding of acronym reading are discussed. The entire set of norms and the acronym reading times (and accuracy scores), together with the acronym definitions, are provided as supplemental materials.

Subjective estimations of age of acquisition (AoA) for a large pool of Spanish words were collected from college students in Spain. The average score for each word (based on 50 individual responses, on a scale from 1 to 11) was taken as an AoA indicator, and normative values for a total of 7,039 single words are provided as supplemental materials. Beyond its intrinsic value as a standalone corpus, the largest of its kind for Spanish, the value of the database is enhanced by the fact that it contains most of the words that are currently included in other normative studies, allowing for a more complete characterization of the lexical stimuli that are usually employed in studies with Spanish-speaking participants. The norms are available for downloading as supplemental materials with this article.

We describe the creation of a massively parallel corpus based on 100 translations of the Bible. We discuss some of the difficulties in acquiring and processing the raw material as well as the potential of the Bible as a corpus for natural language processing. Finally we present a statistical analysis of the corpora collected and a detailed comparison between the English translation and other English corpora.

Human faces are fundamentally dynamic, but experimental investigations of face perception have traditionally relied on static images of faces. Although naturalistic videos of actors have been used with success in some contexts, much research in neuroscience and psychophysics demands carefully controlled stimuli. In this article, we describe a novel set of computer-generated, dynamic face stimuli. These grayscale faces are tightly controlled for low- and high-level visual properties. All faces are standardized in terms of size, luminance, location, and the size of facial features. Each face begins with a neutral pose and transitions to an expression over the course of 30 frames. Altogether, 222 stimuli were created, spanning three different categories of movement: (1) an affective movement (fearful face), (2) a neutral movement (close-lipped, puffed cheeks with open eyes), and (3) a biologically impossible movement (upward dislocation of eyes and mouth). To determine whether early brain responses sensitive to low-level visual features differed between the expressions, we measured the occipital P100 event-related potential, which is known to reflect differences in early stages of visual processing, and the N170, which reflects structural encoding of faces. We found no differences between the faces at the P100, indicating that different face categories were well matched on low-level image properties. This database provides researchers with a well-controlled set of dynamic faces, controlled for low-level image characteristics, that are applicable to a range of research questions in social perception.

Gestures are commonly used together with spoken language in human communication. One major limitation of gesture investigations in the existing literature lies in the fact that the coding of forms and functions of gestures has not been clearly differentiated. This paper first described a recently developed Database of Speech and GEsture (DoSaGE) based on independent annotation of gesture forms and functions among 119 neurologically unimpaired right-handed native speakers of Cantonese (divided into three age and two education levels), and presented findings of an investigation examining how gesture use was related to age and linguistic performance. Consideration of these two factors, for which normative data are currently very limited or lacking in the literature, is relevant and necessary when one evaluates gesture employment among individuals with and without language impairment. Three speech tasks, including monologue of a personally important event, sequential description, and story-telling, were used for elicitation. The EUDICO Linguistic ANnotator (ELAN) software was used to independently annotate each participant's linguistic information of the transcript, forms of gestures used, and the function for each gesture. About one-third of the subjects did not use any co-verbal gestures. While the majority of gestures were non-content-carrying, which functioned mainly for reinforcing speech intonation or controlling speech flow, the content-carrying ones were used to enhance speech content. Furthermore, individuals who are younger or linguistically more proficient tended to use fewer gestures, suggesting that normal speakers gesture differently as a function of age and linguistic performance.

Lexvo.org brings information about languages, words, and other linguistic entities to the Web of Linked Data. It defines URIs for terms, languages, scripts, and characters, which are not only highly interconnected but also linked to a variety of resources on the Web. Additionally, new datasets are being published to contribute to the emerging Linked Data Cloud of Language-Related information.

In the present article, we introduce the Nencki Affective Word List (NAWL), created in order to provide researchers with a database of 2,902 Polish words, including nouns, verbs, and adjectives, with ratings of emotional valence, arousal, and imageability. Measures of several objective psycholinguistic features of the words (frequency, grammatical class, and number of letters) are also controlled. The database is a Polish adaptation of the Berlin Affective Word List-Reloaded (BAWL-R; V{\~{o}} et al., Behavior Research Methods 41:534-538, 2009), commonly used to investigate the affective properties of German words. Affective normative ratings were collected from 266 Polish participants (136 women and 130 men). The emotional ratings and psycholinguistic indexes provided by NAWL can be used by researchers to better control the verbal materials they apply and to adjust them to specific experimental questions or issues of interest. The NAWL is freely accessible to the scientific community for noncommercial use as supplementary material to this article.

We present a database of 858 German words from the semantic fields of authority and community, which represent core dimensions of human sociality. The words were selected on the basis of co-occurrence profiles of representative keywords for these semantic fields. All words were rated along five dimensions, each measured by a bipolar semantic-differential scale: Besides the classic dimensions of affective meaning (valence, arousal, and potency), we collected ratings of authority and community with newly developed scales. The results from cluster, correlational, and multiple regression analyses on the rating data suggest a robust negativity bias for authority valuation among German raters recruited via university mailing lists, whereas community ratings appear to be rather unrelated to the well-established affective dimensions. Furthermore, our data involve a strong overall negative correlation-rather than the classical U-shaped distribution-between valence and arousal for socially relevant concepts. Our database provides a valuable resource for research questions at the intersection of cognitive neuroscience and social psychology. It can be downloaded as supplemental materials with this article.

Learner corpora consist of texts produced by non-native speakers. In addition to these texts, some learner corpora also contain error annotations, which can reveal common errors made by language learners, and provide training material for automatic error correction. We present a novel type of error-annotated learner corpus containing sequences of revised essay drafts written by non-native speakers of English. Sentences in these drafts are annotated with comments by language tutors, and are aligned to sentences in subsequent drafts. We describe the compilation process of our corpus, present its encoding in TEI XML, and report agreement levels on the error annotations. Further, we demonstrate the potential of the corpus to facilitate research on textual revision in L2 writing, by conducting a case study on verb tenses using ANNIS, a corpus search and visualization platform.

Research on the emotional, cognitive, and social determinants of moral judgment has surged in recent years. The development of moral foundations theory (MFT) has played an important role, demonstrating the breadth of morality. Moral psychology has responded by investigating how different domains of moral judgment are shaped by a variety of psychological factors. Yet, the discipline lacks a validated set of moral violations that span the moral domain, creating a barrier to investigating influences on judgment and how their neural bases might vary across the moral domain. In this paper, we aim to fill this gap by developing and validating a large set of moral foundations vignettes (MFVs). Each vignette depicts a behavior violating a particular moral foundation and not others. The vignettes are controlled on many dimensions including syntactic structure and complexity making them suitable for neuroimaging research. We demonstrate the validity of our vignettes by examining respondents' classifications of moral violations, conducting exploratory and confirmatory factor analysis, and demonstrating the correspondence between the extracted factors and existing measures of the moral foundations. We expect that the MFVs will be beneficial for a wide variety of behavioral and neuroimaging investigations of moral cognition.

Pictures are often used in studies on memory, perception, and language; normative data are thus needed for such visual stimuli. In the present study, we aimed to obtain normative data for a set of 272 black-and-white pictures from middle-aged and elderly Persian speakers. A total of 206 volunteers were divided into two groups: a middle-aged (40-59 years old) group and an elderly (60 years old and over) group. The groups had similar characteristics in terms of education. Norms for every picture were developed to provide measures of name agreement, image agreement, conceptual familiarity, age of acquisition, and visual complexity. The results revealed that all of these measures vary with age, except for conceptual familiarity.

This article presents valence/pleasantness, activity/arousal, power/dominance, origin, subjective significance, and source-of-experience norms for 1,586 Polish words (primarily nouns), adapted from the Affective Norms for English Words list (1,040 words) and from my own previous research (546 words), regarding the duality-of-mind approach for emotion formation. This is a first attempt at creating affective norms for Polish words. The norms are based on ratings by a total of 1,670 college students (852 females and 818 males) from different Warsaw universities and academies, studying various disciplines in equal proportions (humanities, engineering, and social and natural sciences) using a 9-point Likert Self-Assessment Manikin scale. Each participant assessed 240 words on six different scales (40 words per scale) using a paper-and-pencil group survey procedure. These affective norms for Polish words are a valid and useful tool that will allow researchers to use standard, well-known verbal materials comparable to the materials used in other languages (English, German, Portuguese, Spanish, French, Dutch, etc.). The normative values of the Polish adaptation of affective norms are included in the online supplemental materials for this article.

Since the work of Taft and Forster (1976), a growing literature has examined how English compound words are recognized and organized in the mental lexicon. Much of this research has focused on whether compound words are decomposed during recognition by manipulating the word frequencies of their lexemes. However, many variables may impact morphological processing, including relational semantic variables such as semantic transparency, as well as additional form-related and semantic variables. In the present study, ratings were collected on 629 English compound words for six variables [familiarity, age of acquisition (AoA), semantic transparency, lexeme meaning dominance (LMD), imageability, and sensory experience ratings (SER)]. All of the compound words selected for this study are contained within the English Lexicon Project (Balota et al., 2007), which made it possible to use a regression approach to examine the predictive power of these variables for lexical decision and word naming performance. Analyses indicated that familiarity, AoA, imageability, and SER were all significant predictors of both lexical decision and word naming performance when they were added separately to a model containing the length and frequency of the compounds, as well as the lexeme frequencies. In addition, rated semantic transparency also predicted lexical decision performance. The database of English compound words should be beneficial to word recognition researchers who are interested in selecting items for experiments on compound words, and it will also allow researchers to conduct further analyses using the available data combined with word recognition times included in the English Lexicon Project.

Knowledge of specific characteristics of verbal material is imperative in cognitive research, and this need calls for periodical updating of normative data. With this aim, and considering that the most recent Spanish-language category norms for adults date back to more than 30 years ago, and that they do not include some very common categories, a new normative study was conducted. In this study, production data for exemplars in the 56 categories of Battig and Montague (Journal of Experimental Psychology, 80, 1-46, 1969) were collected from a pool of 284 young adults who were native speakers of Spanish using an exemplar production task. With the goal of providing a useful tool for cognitive research to be conducted with Spanish-speaking samples, indices of frequency, rank, and lexical availability for the exemplars of each category are provided in a computerized database. The norms described are available for downloading as supplemental material with this article.

This paper intends to present a machine readable Romanian language pronunciation dictionary called NaviRo. The dictionary contains 138,500 unique words from the DexOnline dictionary together with their phonetic transcriptions in speech assessment method phonetic alphabet. The development of the pronunciation dictionary and the performed validation tests are also described in the paper. NaviRo pronunciation dictionary is freely available on the project website (http://users.utcluj.ro/~jdomokos/naviro) in plain text, Hidden Markov Model Toolkit and Festival speech synthesis system dictionary format. There are also available for download the used grapheme and phoneme sets and the audio samples for the used phonemes. The use of these resources is completely unrestricted for any research purposes in order to speed up Romanian language speech technology research.

To investigate the differences in communicative activities by the same interlocutors in Japanese (their L1) and in English (their L2), an 8-h multimodal corpus of multiparty conversations was collected. Three subjects participated in each conversational group, and they had conversations on free-flowing and goal-oriented topics in Japanese and in English. Their utterances, eye gazes, and gestures were recorded with microphones, eye trackers, and video cameras. The utterances and eye gazes were manually annotated. Their utterances were transcribed, and the transcriptions of each participant were aligned with those of the others along the time axis. Quantitative analyses were made to compare the communicative activities caused by the differences in conversational languages, the conversation types, and the levels of language expertise in L2. The results reveal different utterance characteristics and gaze patterns that reflect the differences in difficulty felt by the participants in each conversational condition. Both total and average durations of utterances were shorter in their L2 than in their L1 conversations. Differences in eye gazes were mainly found in those toward the information senders: Speakers were gazed at more in their second-language than in their native-language conversations. Our findings on the characteristics of conversations in the second language suggest possible directions for future research in psychology, cognitive science, and human–computer interaction technologies.

The paper describes the combined results of several projects which constitute a basic language resource infrastructure for printed historical Slovene. The IMP language resources consist of a digital library, an annotated corpus and a lexicon, which are interlinked and uniformly encoded following the Text Encoding Initiative Guidelines. The library holds about 650 units (mostly complete books) consisting of facsimiles with 45,000 pages as well as hand-corrected and structured transcriptions. The hand-annotated corpus has 300,000 tokens, where each word is tagged with its modernised word form, lemma, part-of-speech and, in cases of archaic words, its nearest contemporary equivalents. This information was extracted into the lexicon, which also covers an extended target-annotated corpus, resulting in 20,000 lemmas (of these 4,000 archaic) with 50,000 modern word forms and 70,000 attested forms. We have also developed a program to modernise, tag and lemmatise historical Slovene, and annotated the digital library with it, producing an automatically annotated corpus of 15 million words. To serve the humanities, the digital library and lexicon are available for reading and browsing on the web and the corpora via a concordancer. For language technology research and development the resources are available in source TEI XML under the Creative Commons Attribution licence. The paper presents the IMP resources, available from http://nl.ijs.si/imp/, the process of their compilation, encoding and dissemination, and concludes with directions for future research.

The present study introduces the first Spanish database with normative ratings of semantic similarity for 185 word triplets. Each word triplet is constituted by a target word (e.g., guisante [pea]) and two semantically related and nonassociatively related words: a word highly related in meaning to the target (e.g., jud{\'{i}}a [bean]), and a word less related in meaning to the target (e.g., patata [potato]). The degree of meaning similarity was assessed by 332 participants by using a semantic similarity rating task on a 9-point scale. Pairs having a value of semantic similarity ranging from 5 to 9 were classified as being more semantically related, whereas those with values ranging from 2 to 4.99 were considered as being less semantically related. The relative distance between the two pairs for the same target ranged from 0.48 to 5.07 points. Mean comparisons revealed that participants rated the more similar words as being significantly more similar in meaning to the target word than were the less similar words. In addition to the semantic similarity norms, values of concreteness and familiarity of each word in a triplet are provided. The present database can be a very useful tool for scientists interested in designing experiments to examine the role of semantics in language processing. Since the variable of semantic similarity includes a wide range of values, it can be used as either a continuous or a dichotomous variable. The full database is available in the supplementary materials.

We present SUBTLEX-PL, Polish word frequencies based on movie subtitles. In two lexical decision experiments, we compare the new measures with frequency estimates derived from another Polish text corpus that includes predominantly written materials. We show that the frequencies derived from the two corpora perform best in predicting human performance in a lexical decision task if used in a complementary way. Our results suggest that the two corpora may have unequal potential for explaining human performance for words in different frequency ranges and that corpora based on written materials severely overestimate frequencies for formal words. We discuss some of the implications of these findings for future studies comparing different frequency estimates. In addition to frequencies for word forms, SUBTLEX-PL includes measures of contextual diversity, part-of-speech-specific word frequencies, frequencies of associated lemmas, and word bigrams, providing researchers with necessary tools for conducting psycholinguistic research in Polish. The database is freely available for research purposes and may be downloaded from the authors' university Web site at http://crr.ugent.be/subtlex-pl .

For 84 unique topic-vehicle pairs (e.g., knowledge-power), participants produced associated properties for the topics (e.g., knowledge), vehicles (e.g., power), metaphors (knowledge is power), and similes (knowledge is like power). For these properties, we also obtained frequency, saliency, and connotativeness scores (i.e., how much the properties deviated from the denotative or literal meaning). In addition, we examined whether expression type (metaphor vs. simile) impacted the interpretations produced. We found that metaphors activated more salient properties than did similes, but the connotativeness levels for metaphor and simile salient properties were similar. Also, the two types of expressions did not differ across a wide range of measures collected: aptness, conventionality, familiarity, and interpretive diversity scores. Combined with the property lists, these interpretation norms constitute a thorough collection of data about metaphors and similes, employing the same topic-vehicle words, which can be used in psycholinguistic and cognitive neuroscience studies to investigate how the two types of expressions are represented and processed. These norms should be especially useful for studies that examine the online processing and interpretation of metaphors and similes, as well as for studies examining how properties related to metaphors and similes affect the interpretations produced.

Film clips are widely utilized to elicit emotion in a variety of research studies. Normative ratings for scenes selected for these purposes support the idea that selected clips correspond to the intended target emotion, but studies reporting normative ratings are limited. Using an ethnically diverse sample of college undergraduates, selected clips were rated for intensity, discreteness, valence, and arousal. Variables hypothesized to affect the perception of stimuli (i.e., gender, race-ethnicity, and familiarity) were also examined. Our analyses generally indicated that males reacted strongly to positively valenced film clips, whereas females reacted more strongly to negatively valenced film clips. Caucasian participants tended to react more strongly to the film clips, and we found some variation by race-ethnicity across target emotions. Finally, familiarity with the films tended to produce higher ratings for positively valenced film clips, and lower ratings for negatively valenced film clips. These findings provide normative ratings for a useful set of film clips for the study of emotion, and they underscore factors to be considered in research that utilizes scenes from film for emotion elicitation.

We collected sensory experience ratings (SERs) for 1,659 French words in adults. Sensory experience for words is a recently introduced variable that corresponds to the degree to which words elicit sensory and perceptual experiences (Juhasz {\&} Yap Behavior Research Methods, 45, 160-168, 2013; Juhasz, Yap, Dicke, Taylor, {\&} Gullick Quarterly Journal of Experimental Psychology, 64, 1683-1691, 2011). The relationships of the sensory experience norms with other psycholinguistic variables (e.g., imageability and age of acquisition) were analyzed. We also investigated the degree to which SER predicted performance in visual word recognition tasks (lexical decision, word naming, and progressive demasking). The analyses indicated that SER reliably predicted response times in lexical decision, but not in word naming or progressive demasking. The findings are discussed in relation to the status of SER, the role of semantic code activation in visual word recognition, and the embodied view of cognition.

We present a syntactic parser of (transcripts of) spoken Hebrew: a dependency parser of the Hebrew CHILDES database. CHILDES is a corpus of child–adult linguistic interactions. Its Hebrew section has recently been morphologically analyzed and disambiguated, paving the way for syntactic annotation. This paper describes a novel annotation scheme of dependency relations reflecting constructions of child and child-directed Hebrew utterances. A subset of the corpus was annotated with dependency relations according to this scheme, and was used to train two parsers (MaltParser and MEGRASP) with which the rest of the data were parsed. The adequacy of the annotation scheme to the CHILDES data is established through numerous evaluation scenarios. The paper also discusses different annotation approaches to several linguistic phenomena, as well as the contribution of morphological features to the accuracy of parsing.

[Correction Notice: An Erratum for this article was reported in Vol 47(1) of Behavior Research Methods (see record 2015-07948-022). In Table 3, in the column "Lexical Content of the Sentences" in the "Incongruent" row, all five words (“Anger, Disgust, Fear, Happiness, Sadness”) should be replaced by “Neutral”.] Research on emotional speech often requires valid stimuli for assessing perceived emotion through prosody and lexical content. To date, no comprehensive emotional speech database for Persian is officially available. The present article reports the process of designing, compiling, and evaluating a comprehensive emotional speech database for colloquial Persian. The database contains a set of 90 validated novel Persian sentences classified in five basic emotional categories (anger, disgust, fear, happiness, and sadness), as well as a neutral category. These sentences were validated in two experiments by a group of 1,126 native Persian speakers. The sentences were articulated by two native Persian speakers (one male, one female) in three conditions: (1) congruent (emotional lexical content articulated in a congruent emotional voice), (2) incongruent (neutral sentences articulated in an emotional voice), and (3) baseline (all emotional and neutral sentences articulated in neutral voice). The speech materials comprise about 470 sentences. The validity of the database was evaluated by a group of 34 native speakers in a perception test. Utterances recognized better than five times chance performance (71.4 {\%}) were regarded as valid portrayals of the target emotions. Acoustic analysis of the valid emotional utterances revealed differences in pitch, intensity, and duration, attributes that may help listeners to correctly classify the intended emotion. The database is designed to be used as a reliable material source (for both text and speech) in future cross-cultural or cross-linguistic studies of emotional speech, and it is available for academic research purposes free of charge. To access the database, please contact the first author. (PsycINFO Database Record (c) 2015 APA, all rights reserved)(journal abstract)

In this paper we present a language-independent, fully modular and automatic approach to bootstrap a wordnet for a new language by recycling different types of already existing language resources, such as machine-readable dictionaries, parallel corpora, and Wikipedia. The approach, which we apply here to Slovene, takes into account monosemous and polysemous words, general and specialised vocabulary as well as simple and multi-word lexemes. The extracted words are then assigned one or several synset ids, based on a classifier that relies on several features including distributional similarity. Finally, we identify and remove highly dubious (literal, synset) pairs, based on simple distributional information extracted from a large corpus in an unsupervised way. Automatic, manual and task-based evaluations show that the resulting resource, the latest version of the Slovene wordnet, is already a valuable source of lexico-semantic information.

Treebanks, especially the Penn treebank for natural language processing (NLP) in English, play an essential role in both research into and the application of NLP. However, many languages still lack treebanks and building a treebank can be very complicated and difficult. This work has a twofold objective. Firstly, to share our results in constructing a large Vietnamese treebank (VTB) with three levels of annotation including word segmentation, part-of-speech tagging, and syntactic analysis. Major steps in the treebank construction process are described with particular regard to specific Vietnamese properties such as lack of word delimiter and isolation. Those properties make sentences highly syntactically ambiguous, and therefore it is difficult to ensure a high level of agreement among annotators. Various studies of Vietnamese syntax were employed not only to define annotations but also to systematically deal with ambiguities. Annotators were supported by automatic labelling tools, which are based on statistical machine learning methods, for sentence pre-processing and a tree editor for supporting manual annotation. As a result, an annotation agreement of around 90 % was achieved. Our second objective is to present our method for automatically finding errors and inconsistencies in treebank corpora and its application to the construction of the VTB. This method employs the Shannon entropy measure in a manner that the more reduced entropy the more corrected errors in a treebank. The method ranks error candidates by using a scoring function based on conditional entropy. Our experiments showed that this method detected high-error-density subsets of original error candidate sets, and that the corpus entropy was significantly reduced after error correction. The size of these subsets was only about one third of the whole set, while these subsets contained 80–90 % of the total errors. This method can also be applied to languages similar to Vietnamese.

The language used in social media is often characterized by the abundance of informal and non-standard writing. The normalization of this non-standard language can be crucial to facilitate the subsequent textual processing and to consequently help boost the performance of natural language processing tools applied to social media text. In this paper we present a benchmark for lexical normalization of social media posts, specifically for tweets in Spanish language. We describe the tweet normalization challenge we organized recently, analyze the performance achieved by the different systems submitted to the challenge, and delve into the characteristics of systems to identify the features that were useful. The organization of this challenge has led to the production of a benchmark for lexical normalization of social media, including an evaluation framework, as well as an annotated corpus of Spanish tweets—TweetNorm_es—, which we make publicly available. The creation of this benchmark and the evaluation has brought to light the types of words that submitted systems did best with, and posits the main shortcomings to be addressed in future work.

Compared with English, Arabic is a poorly-resourced language within the field of corpus linguistics. A lack of sufficient data and research has negatively affected Arabic corpus-based researchers and natural language processing practitioners. Although a number of Arabic corpora have been developed in recent years, the overall situation has improved little. The aim of this paper is twofold. First, it reviews 14 Arabic corpora categorized by their designated purpose, target language, mode of text, size, text date, location, text type/medium, text domain, representativeness, and balance. The review also describes the availability of the reviewed corpora, the presence of tokenization, lemmatization and tagging, and whether there are any tools available to search and explore them. Second, it introduces the King Abdulaziz City for Science and Technology (KACST) Arabic corpus, which was designed and created to overcome the limitations of existing Arabic corpora. The KACST Arabic corpus is a large and diverse Arabic corpus with clearly defined design criteria. It is carefully sampled, and its contents are classified based on time, region, medium, domain, and topic, and it can be searched and explored using these classifications. The KACST Arabic corpus comprises more than 700 million words from the pre-Islamic era to the present day (a period covering more than 1,500 years), collected from 10 diverse mediums. Each text has been further classified more specifically into domains and topics. The KACST Arabic corpus is freely available to explore on the Internet (http://www.kacstac.org.sa) using a variety of tools.

A corpus of French tales is presented. Its two parts, a text corpus and a speech corpus, were designed for studying the relationships between the textual structures of tales and speech prosody, with the targeted application of an expressive text-to-speech synthesis system embedded in a humanoid robot. The 89-tale text corpus, and the 12-tale speech corpus were annotated using a common tale description framework. Lexical level annotations include extended definitions of enumerations, time, place and person named entities, as well as part of speech tags. Supra-lexical level annotations include the segmentation of tales into a sequence of episodes, the localization and attribution of direct quotations, together with tale protagonists co-references. Annotation distributions and inter-annotator agreement were analyzed. The largest coverage and strongest agreement were observed for person named entities, characters’ direct quotations, and their associated coreference chains. Speech corpus annotations were extended to allow the analysis of the relations between tale linguistic information and prosodic properties observed in associated speech. Word and phoneme boundaries were inferred through semi-automatic procedures, resulting in linguistic annotations aligned with the speech signal. Intonation stylization models were used to ease the visual and statistical analysis of tale’s prosody. Additional meta-information is provided with the speech corpus, allowing describing tale characters according to their gender, age, size, valence and kind. The corpora described in this article are publicly available through the European Language Resources Association catalog.

This paper introduces the South East Asia Mandarin–English corpus, a 63-h spontaneous Mandarin–English code-switching transcribed speech corpus suitable for LVCSR and language change detection/identification research. The corpus is recorded under unscripted interview and conversational settings from 157 Singaporean and Malaysian speakers who spoke a mixture of Mandarin and English within a single sentence. About 82 % of the transcribed utterances are intra-sentential code-switching speech and the corpus will be release by LDC in 2015. This paper presents an analysis of the code-switching statistics of the corpus, such as the duration of monolingual segments and the frequency of language turns in code-switch utterances. We also summarize the development effort, details such as the processing time for transcription, validation and language boundary labelling. Lastly, we present textual analyses of code-switch segments examining the word length of monolingual segments in code-switch utterances and the most common single word and two-word phrase of such segments.

This paper presents the IULA Spanish LSP Treebank, an open-source treebank of over 40,000 sentences, developed in the framework of the European project METANET4U. The IULA Spanish LSP Treebank is the first technical corpus of Spanish annotated at surface syntactic level, following the dependency grammar theory. We present the method we used to create the resource and the linguistic annotations that the treebank provides, using examples and comparing with similar resources. We also provide the statistics of the treebank and the evaluation results.

The paper presents the Chinese Discourse TreeBank, a corpus annotated with Penn Discourse TreeBank style discourse relations that take the form of a predicate taking two arguments. We first characterize the syntactic and statistical distributions of Chinese discourse connectives as well as the role of Chinese punctuation marks in discourse annotation, and then describe how we design our annotation strategy procedure based on this characterization. The Chinese-specific features of our annotation strategy include annotating explicit and implicit discourse relations in one single pass, defining the argument labels on semantic, rather than syntactic, grounds, as well as annotating the semantic type of implicit discourse relations directly. We also introduce a flat, 11-valued semantic type classification scheme for discourse relations. We finally demonstrate the feasibility of our approach with evaluation results.

The role of objects' motor affordances in cognition is a topic that has gained in popularity over the last decades. However, few studies exist that have normed the different motor dimensions of the objects; this limits researchers regarding usable stimuli, as well as comparability between studies. In the present study, we normed a set of 560 objects on four motor dimensions: the ease with which they can be grasped, moved, and pantomimed and the number of actions they afford. We then examined whether these four dimensions predict objects' naming latency. We believe that these norms will allow researchers interested in the role of motor affordances to have a better control over the dimensions they want to manipulate.

The present article introduces a Russian-language database of 375 action pictures and associated verbs with normative data. The pictures were normed for name agreement, conceptual familiarity, and subjective visual complexity, and measures of age of acquisition, imageability, and image agreement were collected for the verbs. Values of objective visual complexity, as well as information about verb frequency, length, argument structure, instrumentality, and name relation, are also provided. Correlations between these parameters are presented, along with a comparative analysis of the Russian name agreement norms and those collected in other languages. The full set of pictorial stimuli and the obtained norms may be freely downloaded from http://neuroling.ru/en/db.htm for use in research and for clinical purposes.

Although many facial and vocal databases are available for research, very few of them have controlled the range of attractiveness of the stimuli that they offer. To fill this gap, we created the GEneva Faces and Voices (GEFAV) database, providing standardized faces (static and dynamic neutral, smiling) and voices (speaking sentences, vowels) of young European adults. A total of 61 women and 50 men 18-35 years old agreed to be part of the GEFAV stimuli, and two rating studies involving 285 participants provided evaluations of the facial and vocal samples. The final set of stimuli was satisfactory in terms of attractiveness range (wide and rather symmetrical distribution over the attractiveness continuum) and the reliability of the ratings (high consistency between the two rating studies, high interrater agreement in the final rating study). Moreover, the database showed an adequate validity, since a series of findings described by earlier research on human attractiveness were confirmed-namely, that facial and vocal attractiveness are predicted by femininity and health in women, and by masculinity, dominance, and trustworthiness in men. In future studies, the GEFAV stimuli may be used intact or transformed, individually or in multimodal combinations, to investigate a wide range of mechanisms, such as the behavioral, neuropsychological, and neurophysiological processes involved in social cognition.

We present the Finnish PropBank, a resource for semantic role labeling (SRL) of Finnish based on the Turku Dependency Treebank whose syntax is annotated in the well-known Stanford Dependency (SD) scheme. The contribution of this paper consists of the lexicon of the verbs and their arguments present in the treebank, as well as the predicate-argument annotation of all verb occurrences in the treebank text. We demonstrate that the annotation is of high quality, that the SD scheme is highly compatible with PropBank annotation, and further that the additional dependencies present in the Turku Dependency Treebank are clearly beneficial for PropBank annotation. Further, we also use the PropBank to provide a strong baseline for automated Finnish SRL using a machine learning SRL system developed for the SemEval’14 shared task on broad-coverage semantic dependency parsing. The PropBank as well as the SRL system are available under a free license at http://bionlp.utu.fi/.

This article introduces childLex, an online database of German read by children. childLex is based on a corpus of children's books and comprises 10 million words that were syntactically annotated and lemmatized. childLex reports linguistic norms for lexical, superlexical, and sublexical variables in three different age groups: 6-8 (grades 1-2), 9-10 (grades 3-4), and 11-12 years (grades 5-6). Here, we describe how childLex was collected and analyzed. In addition, we provide information about the distributions of word frequency, word length, and orthographic neighborhood size, as well as their intercorrelations. Finally, we explain how childLex can be accessed using a Web interface.

Research on the multimodal aspects of interactional language use requires high-quality multimodal resources. In contrast to the vast amount of available written language corpora and collections of transcribed spoken language, truly multimodal corpora including visual as well as auditory data are scarce. In this paper, we first discuss a few notable exceptions that do provide high-quality and multiple-angle video recordings of face-to-face conversations. We then present a new multimodal corpus design that adds two dimensions to the existing resources. First, the recording set-up was designed in such a way as to have a full view of the dialogue partners’ gestural behaviour, including hand gestures, facial expressions and body posture. Second, by recording the participant perspective and behaviour during conversation, using head-mounted scene cameras and eye-trackers, we obtained a 3D landscape of the conversation, with detailed production information (scene camera and sound) and indices of cognitive processing (eye movements for gaze analysis) for both participants. In its current form, the resulting InSight Interaction Corpus consists of 15 recorded face-to-face interactions of 20 min each, of which five have been transcribed and annotated for a range of linguistic and gestural features, using the ELAN multimodal annotation tool.

Researchers studying a range of psychological phenomena (e.g., theory of mind, emotion, stereotyping and prejudice, interpersonal attraction, etc.) sometimes employ photographs of people as stimuli. In this paper, we introduce the Chicago Face Database, a free resource consisting of 158 high-resolution, standardized photographs of Black and White males and females between the ages of 18 and 40 years and extensive data about these targets. In Study 1, we report pre-testing of these faces, which includes both subjective norming data and objective physical measurements of the images included in the database. In Study 2 we surveyed psychology researchers to assess the suitability of these targets for research purposes and explored factors that were associated with researchers' judgments of suitability. Instructions are outlined for those interested in obtaining access to the stimulus set and accompanying ratings and measures.

All words have properties linked to form, meaning and usage patterns which influence how easily they are accessed from the mental lexicon in language production, perception and comprehension. Examples of such properties are imageability, phonological and morphological complexity, word class, argument structure, frequency of use and age of acquisition. Due to linguistic and cultural variation the properties and the values associated with them differ across languages. Hence, for research as well as clinical purposes, language specific information on lexical properties is needed. To meet this need, an electronically searchable lexical database with more than 1600 Norwegian words coded for more than 12 different properties has been established. This article presents the content and structure of the database as well as the search options available in the interface. Finally, it briefly describes some of the ways in which the database can be used in research, clinical practice and teaching. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

We examined the potential advantage of the lexical databases using subtitles and present SUBTLEX-PT, a new lexical database for 132,710 Portuguese words obtained from a 78 million corpus based on film and television series subtitles, offering word frequency and contextual diversity measures. Additionally we validated SUBTLEX-PT with a lexical decision study involving 1920 Portuguese words (and 1920 nonwords) with different lengths in letters (M = 6.89, SD = 2.10) and syllables (M = 2.99, SD = 0.94). Multiple regression analyses on latency and accuracy data were conducted to compare the proportion of variance explained by the Portuguese subtitle word frequency measures with that accounted by the recent written-word frequency database (Procura-PALavras; P-PAL; Soares, Iriarte, et al., 2014). As its international counterparts, SUBTLEX-PT explains approximately 15% more of the variance in the lexical decision performance of young adults than the P-PAL database. Moreover, in line with recent studies, contextual diversity accounted for approximately 2% more of the variance in participants' reading performance than the raw frequency counts obtained from subtitles. SUBTLEX-PT is freely available for research purposes (at http://p-pal.di.uminho.pt/about/databases). (PsycINFO Database Record (c) 2018 APA, all rights reserved)

We present a collection of association norms for 246 German depictable compound nouns and their constituents, comprising 58,652 association tokens distributed over 26,004 stimulus–associate pair types. Analyses of the data revealed that participants mainly provided noun associates, followed by adjective and verb associates. In corpus analyses, co-occurrence values for compounds and their associates were below those for nouns in general and their associates. The semantic relations between compound stimuli and their associates were more often co-hyponymy and hypernymy and less often hyponymy than for associations to nouns in general. Finally, we found a moderate correlation between the overlap of the associations to compounds and their constituents and the degree of semantic transparency. These data represent a collection of associations to German compound nouns and their constituents that constitute a valuable resource concerning the lexical semantic properties of the compound stimuli and the semantic relations between the stimuli and their associates. More specifically, the norms can be used for stimulus selection, hypothesis testing, and further research on morphologically complex words. The norms are available in text format (utf-8 encoding) as supplemental materials.

This article presents a new corpus of 820 words pertaining to 14 semantic categories, 7 natural (animals, body parts, insects, flowers, fruits, trees, and vegetables) and 7 man-made (buildings, clothing, furniture, kitchen utensils, musical instruments, tools, and vehicles); each word in the database was collected empirically in a previous exemplar generation study. In the present study, 152 Spanish speakers provided data for four psycholinguistic variables known to affect lexical-semantic processing in both neurologically intact and brain-damaged participants: age of acquisition, familiarity, manipulability, and typicality. Furthermore, we collected lexical frequency data derived from Internet search hits, plus three additional Spanish lexical frequency indexes. Word length, number of syllables, and the proportion of respondents citing the exemplar as a category member-which can be useful as an additional measure of typicality-are also provided. Reliability and validity indexes showed that our items display characteristics similar to those of other corpora. Overall, this new corpus of words provides a useful tool for scientists engaged in cognitive- and neuroscience-based research focused on examining language, memory, and object processing. The full set of norms can be downloaded from www.psychonomic.org/archive.

This article presents the NeoHelp visual stimulus set created to facilitate investigation of need-of-help recognition with clinical and normative populations of different ages, including children. Need-of-help recognition is one aspect of socioemotional development and a necessary precondition for active helping. The NeoHelp consists of picture pairs showing everyday situations: The first item in a pair depicts a child needing help to achieve a goal; the second one shows the child achieving the goal. Pictures of birds in analogue situations are also included. These control stimuli enable implementation of a human-animal categorization task which serves to separate behavioral correlates specific to need-of-help recognition from general differentiation processes. It is a concern in experimental research to ensure that results do not relate to systematic perceptual differences when comparing responses to categories of different content. Therefore, we not only derived the NeoHelp-pictures within a pair from one another by altering as little as possible, but also assessed their perceptual similarity empirically. We show that NeoHelp-picture pairs are very similar regarding low-level perceptual properties across content categories. We obtained data from 60 children in a broad age range (4 to 13 years) for three different paradigms, in order to assess whether the intended categorization and differentiation could be observed reliably in a normative population. Our results demonstrate that children can differentiate the pictures' content regarding both need-of-help category as well as species as intended in spite of the high perceptual similarities. We provide standard response characteristics (hit rates and response times) that are useful for future selection of stimuli and comparison of results across studies. We show that task requirements coherently determine which aspects of the pictures influence response characteristics. Thus, we present NeoHelp, the first open-access standardized visual stimuli set for investigation of need-of-help recognition and invite researchers to use and extend it.

The Chinese language has more native speakers than any other language, but research on the reading of Chinese characters is still not as well-developed as it is for the reading of words in alphabetic languages. Two areas notably lacking are the paucity of megastudies in Chinese and the relatively infrequent use of the lexical decision paradigm to investigate single-character recognition. The Chinese Lexicon Project, described in this article, is a database of lexical decision latencies for 2,500 Chinese single characters in simplified script, collected from a sample of native mainland Chinese (Mandarin) speakers (N = 35). This resource will provide a valuable adjunct to influential mega-databases, such as the English, French, and Dutch Lexicon Projects. Using two separate analyses, some advantages associated with megastudies are exemplified. These include the selection of the strongest measure to represent Chinese character frequency (Cai {\&} Brysbaert's (PLoS ONE 5(6): e10729, 2010) subtitle contextual diversity frequency count), and the conducting of virtual studies to replicate and clarify existing findings. The unique morpho-syllabic nature of the Chinese writing system makes it a valuable case study for functional language contrasts. Moreover, this is the first publicly available large-scale repository of behavioral responses pertaining to Chinese language processing (the behavioral dataset is attached to this article, as a supplemental file available for download). For these reasons, the data should be of substantial interest to psychologists, linguists, and other researchers.

In order to explore the role of the main psycholinguistic variables on visual word recognition, several mega-studies have been conducted in English in recent years. Nevertheless, because the effects of these variables depend on the regularity of the orthographic system, studies must also be done in other languages with different characteristics. The goal of this work was to conduct a lexical decision study in Spanish, a language with a shallow orthography and a high number of words. The influence of psycholinguistic variables on latencies corresponding to 2,765 words was assessed by means of linear mixed-effects modeling. The results show that some variables, such as frequency or age of acquisition, have significant effects on reaction times regardless of the type of words used. Other variables, such as orthographic neighborhood or imageability, were significant only in specific groups of words. Our results highlight the importance of taking into account the peculiarities of each spelling system in the development of reading models.

The present study provides affective norms for a large corpus of French words (N = 1,031) that were rated on emotional valence and emotional arousal by 469 French young adults. Ratings were made using the Self-Assessment Manikin (Lang, 1980). By combining evaluations of valence and arousal, and including ratings provided by male and female young adults, this database complements and extends existing French-language databases. The response reliability for the two affective dimensions was good, and the consistency between the present and previous ratings was high. We found a strong quadratic relationship between the valence and arousal ratings. Perceptions of the affective content of a word were partly linked to sex. This new affective database (FAN) will enable French-speaking researchers to select suitable materials for studies of how the character of affective words influences their cognitive processing. FAN is available as an online supplement downloadable with this article.

Planning, predicting, reasoning, and acting often depend crucially on the correct encoding and application of knowledge concerning the temporal and causal ordering of events. Yet no pictorial stimulus set is optimized for investigating the processing of temporal and causal order information. We introduce a novel stimulus set of 265 black-and-white line drawings depicting a diverse array of recognizable events. Most of the images in the stimulus set (N = 222) share a thematic or conceptual association with one other image in the set, and the stimuli were created and extensively normed such that the image pairs vary in the degrees to which they share a causal, ordered relation with one another. The stimuli were standardized in a series of normative tasks, including concept/noun/verb agreement, perceived frequency, visual similarity, and indexes of three features of causal associations between events (i.e., temporal proximity, exclusivity, and priority). Both younger adults (ages 18-30 years) and older adults (ages 60-80 years) contributed normative data, allowing for broad applications of the stimuli to the study of normal and age-related changes in the encoding, retention, and retrieval of information regarding temporal and causal order. Complete normative data sets are available in the online supplemental materials, and the full stimulus set is available by contacting the first author.

We present the KELLY project and its work on developing monolingual and bilingual word lists for language learning, using corpus methods, for nine languages and thirty-six language pairs. We describe the method and discuss the many challenges encountered. We have loaded the data into an online database to make it accessible for anyone to explore and we present our own first explorations of it. The focus of the paper is thus twofold, covering pedagogical and methodological aspects of the lists’ construction, and linguistic aspects of the by-product of the project, the KELLY database.

Emotion expression in human-human interaction takes place via various types of information, including body motion. Research on the perceptual-cognitive mechanisms underlying the processing of natural emotional body language can benefit greatly from datasets of natural emotional body expressions that facilitate stimulus manipulation and analysis. The existing databases have so far focused on few emotion categories which display predominantly prototypical, exaggerated emotion expressions. Moreover, many of these databases consist of video recordings which limit the ability to manipulate and analyse the physical properties of these stimuli. We present a new database consisting of a large set (over 1400) of natural emotional body expressions typical of monologues. To achieve close-to-natural emotional body expressions, amateur actors were narrating coherent stories while their body movements were recorded with motion capture technology. The resulting 3-dimensional motion data recorded at a high frame rate (120 frames per second) provides fine-grained information about body movements and allows the manipulation of movement on a body joint basis. For each expression it gives the positions and orientations in space of 23 body joints for every frame. We report the results of physical motion properties analysis and of an emotion categorisation study. The reactions of observers from the emotion categorisation study are included in the database. Moreover, we recorded the intended emotion expression for each motion sequence from the actor to allow for investigations regarding the link between intended and perceived emotions. The motion sequences along with the accompanying information are made available in a searchable MPI Emotional Body Expression Database. We hope that this database will enable researchers to study expression and perception of naturally occurring emotional body expressions in greater depth.

In this study, we present the normative values of the adaptation of the International Affective Digitized Sounds (IADS-2; Bradley {\&} Lang, 2007a) for European Portuguese (EP). The IADS-2 is a standardized database of 167 naturally occurring sounds that is widely used in the study of emotions. The sounds were rated by 300 college students who were native speakers of EP, in the three affective dimensions of valence, arousal, and dominance, by using the Self-Assessment Manikin (SAM). The aims of this adaptation were threefold: (1) to provide researchers with standardized and normatively rated affective sounds to be used with an EP population; (2) to investigate sex and cultural differences in the ratings of affective dimensions of auditory stimuli between EP and the American (Bradley {\&} Lang, 2007a) and Spanish (Fern{\'{a}}ndez-Abascal et al., Psicothema 20:104-113 2008; Redondo, Fraga, Padr{\'{o}}n, {\&} Pi{\~{n}}eiro, Behavior Research Methods 40:784-790 2008) standardizations; and (3) to promote research on auditory affective processing in Portugal. Our results indicated that the IADS-2 is a valid and useful database of digitized sounds for the study of emotions in a Portuguese context, allowing for comparisons of its results with those of other international studies that have used the same database for stimulus selection. The normative values of the EP adaptation of the IADS-2 database can be downloaded along with the online version of this article.

We present a verb–complement dictionary of Modern Hebrew, automatically extracted from text corpora. Carefully examining a large set of examples, we defined ten types of verb complements that cover the vast majority of the occurrences of verb complements in the corpora. We explored several collocation measures as indicators of the strength of the association between the verb and its complement. We then used these measures to automatically extract verb complements from corpora. The result is a wide-coverage, accurate dictionary that lists not only the likely complements for each verb, but also the likelihood of each complement. We evaluated the quality of the extracted dictionary both intrinsically and extrinsically. Intrinsically, we showed high precision and recall on randomly (but systematically) selected verbs. Extrinsically, we showed that using the extracted information is beneficial for two applications, prepositional phrase attachment disambiguation and Arabic-to-Hebrew machine translation.

Through this study, we aimed to validate a new tool for inducing moods in experimental contexts. Five audio stories with sad, joyful, frightening, erotic, or neutral content were presented to 60 participants (33 women, 27 men) in a within-subjects design, each for about 10 min. Participants were asked (1) to report their moods before and after listening to each story, (2) to assess the emotional content of the excerpts on various emotional scales, and (3) to rate their level of projection into the stories. The results confirmed our a priori emotional classification. The emotional stories were effective in inducing the desired mood, with no difference found between male and female participants. These stories therefore constitute a valuable corpus for inducing moods in French-speaking participants, and they are made freely available for use in scientific research.

The index of productive syntax (IPSyn; Scarborough (Applied Psycholinguistics 11:1-22, 1990) is a measure of syntactic development in child language that has been used in research and clinical settings to investigate the grammatical development of various groups of children. However, IPSyn is mostly calculated manually, which is an extremely laborious process. In this article, we describe the AC-IPSyn system, which automatically calculates the IPSyn score for child language transcripts using natural language processing techniques. Our results show that the AC-IPSyn system performs at levels comparable to scores computed manually. The AC-IPSyn system can be downloaded from www.hlt.utdallas.edu/{\~{}}nisa/ipsyn.html .

Most efforts at automatically creating multilingual lexicons require input lexical resources with rich content (e.g. semantic networks, domain codes, semantic categories) or large corpora. Such material is often unavailable and difficult to construct for under-resourced languages. In some cases, particularly for some ethnic languages, even unannotated corpora are still in the process of collection. We show how multilingual lexicons with under-resourced languages can be constructed using simple bilingual translation lists, which are more readily available. The prototype multilingual lexicon developed comprise six member languages: English, Malay, Chinese, French, Thai and Iban, the last of which is an under-resourced language in Borneo. Quick evaluations showed that 91.2  % of 500 random multilingual entries in the generated lexicon require minimal or no human correction.

In this paper, we present the final version of a publicly available treebank of Finnish, the Turku Dependency Treebank. The treebank contains 204,399 tokens (15,126 sentences) from 10 different text sources and has been manually annotated in a Finnish-specific version of the well-known Stanford Dependency scheme. The morphological analyses of the treebank have been assigned using a novel machine learning method to disambiguate readings given by an existing tool. As the second main contribution, we present the first open source Finnish dependency parser, trained on the newly introduced treebank. The parser achieves a labeled attachment score of 81 %. The treebank data as well as the parsing pipeline are available under an open license at http://bionlp.utu.fi/.

We present a database of high-definition (HD) videos for the study of traits inferred from whole-body actions. Twenty-nine actors (19 female) were filmed performing different actions—walking, picking up a box, putting down a box, jumping, sitting down, and standing and acting—while conveying different traits, including four emotions (anger, fear, happiness, sadness), untrustworthiness, and neutral, where no specific trait was conveyed. For the actions conveying the four emotions and untrustworthiness, the actions were filmed multiple times, with the actor conveying the traits with different levels of intensity. In total, we made 2,783 action videos (in both two-dimensional and three-dimensional format), each lasting 7 s with a frame rate of 50 fps. All videos were filmed in a green-screen studio in order to isolate the action information from all contextual detail and to provide a flexible stimulus set for future use. In order to validate the traits conveyed by each action, we asked participants to rate each of the actions corresponding to the trait that the actor portrayed in the two-dimensional videos. To provide a useful database of stimuli of multiple actions conveying multiple traits, each video name contains information on the gender of the actor, the action executed, the trait conveyed, and the rating of its perceived intensity. All videos can be downloaded free at the following address: http://www-users.york.ac.uk/{\~{}}neb506/databases.html. We discuss potential uses for the database in the analysis of the perception of whole-body actions.

In the present study, we present normative ratings of free association for 139 European Portuguese (EP) words among 7- to 8-, 9- to 10-, and 11- to 12-year-old children attending the 3rd, 5th, and 7th grades of elementary and middle school in Portugal. For each word, five indices are presented: (a) the percentage of associates, (b) the strength of the first associate, (c) the strength of the second associate, (d) the distance between the first and second associates, and (e) the percentage of idiosyncratic responses. Additionally, grade-level frequency values for each word from the ESCOLEX database (Soares et al., in press) are also provided. As expected, the results revealed developmental changes in the knowledge organization of the children, which occurred at the ages of 9–10 (5th grade) and remained stable in the 11- to 12-year-old children (7th grade). Specifically, we observed a decrease in the percentages of associates and idiosyncratic responses, as well as an increase in the strengths of the first and second associates from the 3rd to the 5th grade. Moreover, a comparative analysis with the previous work of Carneiro, Albuquerque, Fernandez, and Esteves (2004) on EP and Macizo, G{\'{o}}mez-Ariza, and Bajo (2000) on Spanish, for the subsets of common words (16 and 58, respectively), showed that the present norms fit well with previous EP data, but differ from the Spanish data.

Concreteness ratings are presented for 37,058 English words and 2,896 two-word expressions (such as zebra crossing and zoom in), obtained from over 4,000 participants by means of a norming study using Internet crowdsourcing for data collection. Although the instructions stressed that the assessment of word concreteness would be based on experiences involving all senses and motor responses, a comparison with the existing concreteness norms indicates that participants, as before, largely focused on visual and haptic experiences. The reported data set is a subset of a comprehensive list of English lemmas and contains all lemmas known by at least 85 {\%} of the raters. It can be used in future research as a reference list of generally known English lemmas.

The need for data about the acquisition of Czech by non-native learners prompted the compilation of the first learner corpus of Czech. After introducing its basic design and parameters, including a multi-tier manual annotation scheme and error taxonomy, we focus on the more technical aspects: the transcription of hand-written source texts, process of annotation, and options for exploiting the result, together with tools used for these tasks and decisions behind the choices. To support or even substitute manual annotation we assign some error tags automatically and use automatic annotation tools (tagger, spell checker).

Starting in 2006, the European Commission’s Joint Research Centre and other European Union organisations have made available a number of large-scale highly-multilingual parallel language resources. In this article, we give a comparative overview of these resources and we explain the specific nature of each of them. This article provides answers to a number of question, including: What are these linguistic resources? What is the difference between them? Why were they originally created and why was the data released publicly? What can they be used for and what are the limitations of their usability? What are the text types, subject domains and languages covered? How to avoid overlapping document sets? How do they compare regarding the formatting and the translation alignment? What are their usage conditions? What other types of multilingual linguistic resources does the EU have? This article thus aims to clarify what the similarities and differences between the various resources are and what they can be used for. It will also serve as a reference publication for those resources, for which a more detailed description has been lacking so far (EAC-TM, ECDC-TM and DGT-Acquis).

The balanced corpus of contemporary written Japanese (BCCWJ) is Japan’s first 100 million words balanced corpus. It consists of three subcorpora (publication subcorpus, library subcorpus, and special-purpose subcorpus) and covers a wide range of text registers including books in general, magazines, newspapers, governmental white papers, best-selling books, an internet bulletin-board, a blog, school textbooks, minutes of the national diet, publicity newsletters of local governments, laws, and poetry verses. A random sampling technique is utilized whenever possible in order to maximize the representativeness of the corpus. The corpus is annotated in terms of dual POS analysis, document structure, and bibliographical information. The BCCWJ is currently accessible in three different ways including Chunagon a web-based interface to the dual POS analysis data. Lastly, results of some pilot evaluation of the corpus with respect to the textual diversity are reported. The analyses include POS distribution, word-class distribution, entropy of orthography, sentence length, and variation of the adjective predicate. High textual diversity is observed in all these analyses.

Virtually no valid materials are available to evaluate confrontation naming in Spanish-English bilingual adults in the U.S. In a recent study, a large group of young Spanish-English bilingual adults were evaluated on An Object and Action Naming Battery (Edmonds {\&} Donovan in Journal of Speech, Language, and Hearing Research 55:359-381, 2012). Rasch analyses of the responses resulted in evidence for the content and construct validity of the retained items. However, the scope of that study did not allow for extensive examination of individual item characteristics, group analyses of participants, or the provision of testing and scoring materials or raw data, thereby limiting the ability of researchers to administer the test to Spanish-English bilinguals and to score the items with confidence. In this study, we present the in-depth information described above on the basis of further analyses, including (1) online searchable spreadsheets with extensive empirical (e.g., accuracy and name agreeability) and psycholinguistic item statistics; (2) answer sheets and instructions for scoring and interpreting the responses to the Rasch items; (3) tables of alternative correct responses for English and Spanish; (4) ability strata determined for all naming conditions (English and Spanish nouns and verbs); and (5) comparisons of accuracy across proficiency groups (i.e., Spanish dominant, English dominant, and balanced). These data indicate that the Rasch items from An Object and Action Naming Battery are valid and sensitive for the evaluation of naming in young Spanish-English bilingual adults. Additional information based on participant responses for all of the items on the battery can provide researchers with valuable information to aid in stimulus development and response interpretation for experimental studies in this population.

We present the German adaptation of the Affective Norms for English Words (ANEW; Bradley {\&} Lang in Technical Report No. C-1. Gainsville: University of Florida, Center for Research in Psychophysiology). A total of 1,003 Words-German translations of the ANEW material-were rated on a total of six dimensions: The classic ratings of valence, arousal, and dominance (as in the ANEW corpus) were extended with additional arousal ratings using a slightly different scale (see BAWL: V{\~{o}} et al. in Behavior Research Methods 41: 531-538, 2009; V{\~{o}}, Jacobs, {\&} Conrad in Behavior Research Methods 38: 606-609, 2006), along with ratings of imageability and potency. Measures of several objective psycholinguistic variables (different types of word frequency counts, grammatical class, number of letters, number of syllables, and number of orthographic neighbors) for the words were also added, so as to further facilitate the use of this new database in psycholinguistic research. These norms can be downloaded as supplemental materials with this article.

Defining the specific role of the factors that affect metaphor processing is a fundamental step for fully understanding figurative language comprehension, either in discourse and conversation or in reading poems and novels. This study extends the currently available materials on everyday metaphorical expressions by providing the first dataset of metaphors extracted from literary texts and scored for the major psycholinguistic variables, considering also the effect of context. A set of 115 Italian literary metaphors presented in isolation (Experiment 1) and a subset of 65 literary metaphors embedded in their original texts (Experiment 2) were rated on several dimensions (word and phrase frequency, readability, cloze probability, familiarity, concreteness, difficulty and meaningfulness). Overall, literary metaphors scored around medium-low values on all dimensions in both experiments. Collected data were subjected to correlation analysis, which showed the presence of a strong cluster of variables-mainly familiarity, difficulty, and meaningfulness-when literary metaphor were presented in isolation. A weaker cluster was observed when literary metaphors were presented in the original contexts, with familiarity no longer correlating with meaningfulness. Context manipulation influenced familiarity, concreteness and difficulty ratings, which were lower in context than out of context, while meaningfulness increased. Throughout the different dimensions, the literary context seems to promote a global interpretative activity that enhances the open-endedness of the metaphor as a semantic structure constantly open to all possible interpretations intended by the author and driven by the text. This dataset will be useful for the design of future experimental studies both on literary metaphor and on the role of context in figurative meaning, combining ecological validity and aesthetic aspects of language.

We collected norms on the gender stereotypicality of an extensive list of role nouns in Czech, English, French, German, Italian, Norwegian, and Slovak, to be used as a basis for the selection of stimulus materials in future studies. We present a Web-based tool (available at https://www.unifr.ch/lcg/ ) that we developed to collect these norms and that we expect to be useful for other researchers, as well. In essence, we provide (a) gender stereotypicality norms across a number of languages and (b) a tool to facilitate cross-language as well as cross-cultural comparisons when researchers are interested in the investigation of the impact of stereotypicality on the processing of role nouns.

The present study provides Dutch norms for age of acquisition, familiarity, imageability, image agreement, visual complexity, word frequency, and word length (in syllables) for 124 line drawings of actions. Ratings were obtained from 117 Dutch participants. Word frequency was determined on the basis of the SUBTLEX-NL corpus (Keuleers, Brysbaert, {\&} New, Behavior Research Methods, 42, 643-650, 2010). For 104 of the pictures, naming latencies and name agreement were determined in a separate naming experiment with 74 native speakers of Dutch. The Dutch norms closely corresponded to the norms for British English. Multiple regression analysis showed that age of acquisition, imageability, image agreement, visual complexity, and name agreement were significant predictors of naming latencies, whereas word frequency and word length were not. Combined with the results of a principal-component analysis, these findings suggest that variables influencing the processes of conceptual preparation and lexical selection affect latencies more strongly than do variables influencing word-form encoding.

The Database for Spoken German (Datenbank f{\"{u}}r Gesprochenes Deutsch, DGD2, http://dgd.ids-mannheim.de) is the central platform for publishing and disseminating spoken language corpora from the Archive of Spoken German (Archiv f{\"{u}}r Gesprochenes Deutsch, AGD, http://agd.ids-mannheim.de) at the Institute for the German Language in Mannheim. The corpora contained in the DGD2 come from a variety of sources, some of them in-house projects, some of them external projects. Most of the corpora were originally intended either for research into the (dialectal) variation of German or for studies in conversation analysis and related fields. The AGD has taken over the task of permanently archiving these resources and making them available for reuse to the research community. To date, the DGD2 offers access to 19 different corpora, totalling around 9000 speech events, 2500 hours of audio recordings or 8 million transcribed words. This paper gives an overview of the data made available via the DGD2, of the technical basis for its implementation, and of the most important functionalities it offers. The paper concludes with information about the users of the database and future plans for its development.

Selecting appropriate stimuli to induce emotional states is essential in affective research. Only a few standardized affective stimulus databases have been created for auditory, language, and visual materials. Numerous studies have extensively employed these databases using both behavioral and neuroimaging methods. However, some limitations of the existing databases have recently been reported, including limited numbers of stimuli in specific categories or poor picture quality of the visual stimuli. In the present article, we introduce the Nencki Affective Picture System (NAPS), which consists of 1,356 realistic, high-quality photographs that are divided into five categories (people, faces, animals, objects, and landscapes). Affective ratings were collected from 204 mostly European participants. The pictures were rated according to the valence, arousal, and approach-avoidance dimensions using computerized bipolar semantic slider scales. Normative ratings for the categories are presented for each dimension. Validation of the ratings was obtained by comparing them to ratings generated using the Self-Assessment Manikin and the International Affective Picture System. In addition, the physical properties of the photographs are reported, including luminance, contrast, and entropy. The new database, with accompanying ratings and image parameters, allows researchers to select a variety of visual stimulus materials specific to their experimental questions of interest. The NAPS system is freely accessible to the scientific community for noncommercial use by request at http://naps.nencki.gov.pl .

Reading involves a process of matching an orthographic input with stored representations in lexical memory. The masked priming paradigm has become a standard tool for investigating this process. Use of existing results from this paradigm can be limited by the precision of the data and the need for cross-experiment comparisons that lack normal experimental controls. Here, we present a single, large, high-precision, multicondition experiment to address these problems. Over 1,000 participants from 14 sites responded to 840 trials involving 28 different types of orthographically related primes (e.g., castfe-CASTLE) in a lexical decision task, as well as completing measures of spelling and vocabulary. The data were indeed highly sensitive to differences between conditions: After correction for multiple comparisons, prime type condition differences of 2.90 ms and above reached significance at the 5{\%} level. This article presents the method of data collection and preliminary findings from these data, which included replications of the most widely agreed-upon differences between prime types, further evidence for systematic individual differences in susceptibility to priming, and new evidence regarding lexical properties associated with a target word's susceptibility to priming. These analyses will form a basis for the use of these data in quantitative model fitting and evaluation and for future exploration of these data that will inform and motivate new experiments.

The paper describes a corpus of texts produced by non-native speakers of Czech. We discuss its annotation scheme, consisting of three interlinked tiers, designed to handle a wide range of error types present in the input. Each tier corrects different types of errors; links between the tiers allow capturing errors in word order and complex discontinuous expressions. Errors are not only corrected, but also classified. The annotation scheme is tested on a data set including approx. 175,000 words with fair inter-annotator agreement results. We also explore the possibility of applying automated linguistic annotation tools (taggers, spell checkers and grammar checkers) to the learner text to support or even substitute manual annotation.

Modern paraphrase research would benefit from large corpora with detailed annotations. However, currently these corpora are still thin on the ground. In this paper, we describe the development of such a corpus for Dutch, which takes the form of a parallel monolingual treebank consisting of over 2 million tokens and covering various text genres, including both parallel and comparable text. This publicly available corpus is richly annotated with alignments between syntactic nodes, which are also classified using five different semantic similarity relations. A quarter of the corpus is manually annotated, and this informs the development of an automatic tree aligner used to annotate the remainder of the corpus. We argue that this corpus is the first of this size and kind, and offers great potential for paraphrasing research.

This paper describes the creation of a fine-grained named entity annotation scheme and corpus for Dutch, and experiments on automatic main type and subtype named entity recognition. We give an overview of existing named entity annotation schemes, and motivate our own, which describes six main types (persons, organizations, locations, products, events and miscellaneous named entities) and finer-grained information on subtypes and metonymic usage. This was applied to a one-million-word subset of the Dutch SoNaR reference corpus. The classifier for main type named entities achieves a micro-averaged F-score of 84.91 %, and is publicly available, along with the corpus and annotations.

Automatic methods for wordnet development in languages other than English generally exploit information found in Princeton WordNet (PWN) and translations extracted from parallel corpora. A common approach consists in preserving the structure of PWN and transferring its content in new languages using alignments, possibly combined with information extracted from multilingual semantic resources. Even if the role of PWN remains central in this process, these automatic methods offer an alternative to the manual elaboration of new wordnets. However, their limited coverage has a strong impact on that of the resulting resources. Following this line of research, we apply a cross-lingual word sense disambiguation method to wordnet development. Our approach exploits the output of a data-driven sense induction method that generates sense clusters in new languages, similar to wordnet synsets, by identifying word senses and relations in parallel corpora. We apply our cross-lingual word sense disambiguation method to the task of enriching a French wordnet resource, the WOLF, and show how it can be efficiently used for increasing its coverage. Although our experiments involve the English–French language pair, the proposed methodology is general enough to be applied to the development of wordnet resources in other languages for which parallel corpora are available. Finally, we show how the disambiguation output can serve to reduce the granularity of new wordnets and the degree of polysemy present in PWN.

Dependency grammar is considered appropriate for many Indian languages. In this paper, we present a study of the dependency relations in Bangla language. We have categorized these relations in three different levels, namely intrachunk relations, interchunk relations and interclause relations. Each of these levels is further categorized and an annotation scheme has been developed. Both syntactic and semantic features have been taken into consideration for describing the relations. In our scheme, there are 63 such syntactico–semantic relations. We have verified the scheme by tagging a corpus of 4167 Bangla sentences to create a treebank (KGPBenTreebank).

Word processing studies increasingly make use of regression analyses based on large numbers of stimuli (the so-called megastudy approach) rather than experimental designs based on small factorial designs. This requires the availability of word features for many words. Following similar studies in English, we present and validate ratings of age of acquisition and concreteness for 30,000 Dutch words. These include nearly all lemmas language researchers are likely to be interested in. The ratings are freely available for research purposes.

We present HamleDT—a HArmonized Multi-LanguagE Dependency Treebank. HamleDT is a compilation of existing dependency treebanks (or dependency conversions of other treebanks), transformed so that they all conform to the same annotation style. In the present article, we provide a thorough investigation and discussion of a number of phenomena that are comparable across languages, though their annotation in treebanks often differs. We claim that transformation procedures can be designed to automatically identify most such phenomena and convert them to a unified annotation style. This unification is beneficial both to comparative corpus linguistics and to machine learning of syntactic parsing.

The learner translation corpus developed at the School of Translation and Interpreting of Pompeu Fabra University in Barcelona is a web-searchable resource created for pedagogical and research purposes. It comprises a multiple translation corpus (English–Catalan) featuring automatic linguistic annotation and manual error annotation, complemented with an interface for monolingual or bilingual querying of the data. The corpus can be used to identify common errors in the students’ work and to analyse their patterns of language use. It provides easy access to error samples and to multiple versions of the same source text sequence to be used as learning materials in various courses in the translator-training university curriculum.

A wordnet is an important tool for developing natural language processing applications for a language. However, most wordnets are handcrafted by experts, which limits their growth. In this article, we propose an automatic approach to create wordnets by exploiting textual resources, dubbed ECO. After extracting semantic relation instances, identified by discriminating textual patterns, ECO discovers synonymy clusters, used as synsets, and attaches the remaining relations to suitable synsets. Besides introducing each step of ECO, we report on how it was implemented to create Onto.PT, a public lexical ontology for Portuguese. Onto.PT is the result of the automatic exploitation of Portuguese dictionaries and thesauri, and it aims to minimise the main limitations of existing Portuguese lexical knowledge bases.

The processing of human and nonhuman concepts (e.g., agreeable vs. edible) during basic comprehension and reasoning tasks has become a major topic of scientific inquiry. To ensure that the experimental effects obtained from such studies reflect the hypothesised semantic distinction, potential confounds such as psycholinguistic and/or lexical properties of the exact stimuli chosen need to be addressed. In the current study, normative data of such properties were obtained for a series of 875 French adjectives by asking 8 groups of 20 participants to each rate all words on one dimension of theoretical interest. The collected ratings indicate the extent to which each adjective evokes a sensory experience (concreteness), captures an enduring attribute (temporal stability), refers to a visible characteristic (visibility), denotes a neutral or an affectively laden concept (valence), signifies an attribute of low or high intensity, is familiar to the reader and can be used to describe people and/or inanimate entities such as objects. In addition, for each item its exact grammatical class (adjective vs. past participle adjective), length (i.e., number of letters, number of syllables), and word frequency was retrieved from the lexique3 corpus. The resulting database enables researchers to consider pivotal psycholinguistic and lexical properties when selecting human and nonhuman stimuli for future research.

Theories of the representation and processing of concepts have been greatly enhanced by models based on information available in semantic property norms. This information relates both to the identity of the features produced in the norms and to their statistical properties. In this article, we introduce a new and large set of property norms that are designed to be a more flexible tool to meet the demands of many different disciplines interested in conceptual knowledge representation, from cognitive psychology to computational linguistics. As well as providing all features listed by 2 or more participants, we also show the considerable linguistic variation that underlies each normalized feature label and the number of participants who generated each variant. Our norms are highly comparable with the largest extant set (McRae, Cree, Seidenberg, {\&} McNorgan, 2005) in terms of the number and distribution of features. In addition, we show how the norms give rise to a coherent category structure. We provide these norms in the hope that the greater detail available in the Centre for Speech, Language and the Brain norms should further promote the development of models of conceptual knowledge. The norms can be downloaded at www.csl.psychol.cam.ac.uk/propertynorms.

Formal and semantic overlap across languages plays an important role in bilingual language processing systems. In the present study, Japanese (first language; L1)–English (second language; L2) bilinguals rated 193 Japanese–English word pairs, including cognates and noncognates, in terms of phonological and semantic similarity. We show that the degree of cross-linguistic overlap varies, such that words can be more or less “cognate,” in terms of their phonological and semantic overlap. Bilinguals also translated these words in both directions (L1–L2 and L2–L1), providing a measure of translation equivalency. Notably, we reveal for the first time that Japanese–English cognates are “special,” in the sense that they are usually translated using one English term (e.g., コール /kooru/ is always translated as “call”), but the English word is translated into a greater variety of Japanese words. This difference in translation equivalency likely extends to other nonetymologically related, different-script languages in which cognates are all loanwords (e.g., Korean–English). Norming data were also collected for L1 age of acquisition, L1 concreteness, and L2 familiarity, because such information had been unavailable for the item set. Additional information on L1/L2 word frequency, L1/L2 number of senses, and L1/L2 word length and number of syllables is also provided. Finally, correlations and characteristics of the cognate and noncognate items are detailed, so as to provide a complete overview of the lexical and semantic characteristics of the stimuli. This creates a comprehensive bilingual data set for these different-script languages and should be of use in bilingual word recognition and spoken language research.

In this article, we present the first open-access lexical database that provides phonological representations for 120,000 Italian word forms. Each of these also includes syllable boundaries and stress markings and a comprehensive range of lexical statistics. Using data derived from this lexicon, we have also generated a set of derived databases and provided estimates of positional frequency use for Italian phonemes, syllables, syllable onsets and codas, and character and phoneme bigrams. These databases are freely available from phonitalia.org. This article describes the methods, content, and summarizing statistics for these databases. In a first application of this database, we also demonstrate how the distribution of phonological substitution errors made by Italian aphasic patients is related to phoneme frequency.

We developed affective norms for 1,121 Italian words in order to provide researchers with a highly controlled tool for the study of verbal processing. This database was developed from translations of the 1,034 English words present in the Affective Norms for English Words (ANEW; Bradley {\&} Lang, 1999) and from words taken from Italian semantic norms (Montefinese, Ambrosini, Fairfield, {\&} Mammarella, Behavior Research Methods, 45, 440–461, 2013). Participants evaluated valence, arousal, and dominance using the Self-Assessment Manikin (SAM) in a Web survey procedure. Participants also provided evaluations of three subjective psycholinguistic indexes (familiarity, imageability, and concreteness), and five objective psycholinguistic indexes (e.g., word frequency) were also included in the resulting database in order to further characterize the Italian words. We obtained a typical quadratic relation between valence and arousal, in line with previous findings. We also tested the reliability of the present ANEW adaptation for Italian by comparing it to previous affective databases and performing split-half correlations for each variable. We found high split-half correlations within our sample and high correlations between our ratings and those of previous studies, confirming the validity of the adaptation of ANEW for Italian. This database of affective norms provides a tool for future research about the effects of emotion on human cognition.

This paper introduces the Corpus of Advanced Learner Finnish (LAS2), one of the existing corpora of learner Finnish. The corpus was started at the University of Turku in 2007, and the initial motivation for its collection was to make it possible to deal with novel linguistic challenges posed by academic immigration and to contribute to corpus linguistics, Finnish linguistics and the study of second language acquisition. This paper describes the typological standpoint of the LAS2, its position with respect to other corpora of learner Finnish, the compilation criteria, the annotation applied and the workflow implemented. The corpus consists of three subcorpora of written academic texts of non-native speakers of Finnish. The subcorpora are 1) texts for examination purposes, 2) texts for publishing and graduating purposes, and 3) texts for studying and learning purposes. The informants either study or work in Finnish within academia in Finland. When available, the data has been collected longitudinally. A reference corpus for each subcorpus written by native speakers has also been compiled. Three query tools designed within the framework of the LAS2 are also introduced. These tools enable queries based on any combinations of the linguistic annotation. They can also be used to analyse the typical inner or cotextual variation of any user-specified linguistic node or to create frequency lists of multiword units defined at any level of the annotation. The queries can be limited to a user-specified subset of the data.

Naturalistic learner productions are an important empirical resource for SLA research. Some pioneering works have produced valuable second language (L2) resources supporting SLA research.1 One common limitation of these resources is the absence of individual longitudinal data for numerous speakers with different backgrounds across the proficiency spectrum, which is vital for understanding

We present word frequencies based on subtitles of British television programmes. We show that the SUBTLEX-UK word frequencies explain more of the variance in the lexical decision times of the British Lexicon Project than the word frequencies based on the British National Corpus and the SUBTLEX-US frequencies. In addition to the word form frequencies, we also present measures of contextual diversity part-of-speech specific word frequencies, word frequencies in children programmes, and word bigram frequencies, giving researchers of British English access to the full range of norms recently made available for other languages. Finally, we introduce a new measure of word frequency, the Zipf scale, which we hope will stop the current misunderstandings of the word frequency effect.

In this article, we introduce ESCOLEX, the first European Portuguese children's lexical database with grade-level-adjusted word frequency statistics. Computed from a 3.2-million-word corpus, ESCOLEX provides 48,381 word forms extracted from 171 elementary and middle school textbooks for 6- to 11-year-old children attending the first six grades in the Portuguese educational system. Like other children's grade-level databases (e.g., Carroll, Davies, {\&} Richman, 1971; Corral, Ferrero, {\&} Goikoetxea, Behavior Research Methods, 41, 1009–1017, 2009; L{\'{e}}t{\'{e}}, Sprenger-Charolles, {\&} Col{\'{e}}, Behavior Research Methods, Instruments, {\&} Computers, 36, 156–166, 2004; Zeno, Ivens, Millard, Duvvuri, 1995), ESCOLEX provides four frequency indices for each grade: overall word frequency (F), index of dispersion across the selected textbooks (D), estimated frequency per million words (U), and standard frequency index (SFI). It also provides a new measure, contextual diversity (CD). In addition, the number of letters in the word and its part(s) of speech, number of syllables, syllable structure, and adult frequencies taken from P-PAL (a European Portuguese corpus-based lexical database; Soares, Comesa{\~{n}}a, Iriarte, Almeida, Sim{\~{o}}es, Costa, {\ldots}, Machado, 2010; Soares, Iriarte, Almeida, Sim{\~{o}}es, Costa, Fran{\c{c}}a, {\ldots}, Comesa{\~{n}}a, in press) are provided. ESCOLEX will be a useful tool both for researchers interested in language processing and development and for professionals in need of verbal materials adjusted to children's developmental stages. ESCOLEX can be downloaded along with this article or from http://​p-pal.​di.​uminho.​pt/​about/​databases.

In the present study, normative data in Turkish are presented for the 260 color versions of the original Snodgrass and Vanderwart (1980) picture set for the first time. Norms are reported for name and image agreement, age of acquisition (AoA), visual complexity, and conceptual familiarity, together with written word frequency, and numbers of letters and syllables. We collected data from 277 native Turkish adults in a variety of tasks. The results indicated that, whilst several measures displayed language-specific variation, we also reported what seem to be language-independent-that is, universal-measures that show a systematic relationship across several languages. The implications of the reported measures in the domain of psycholinguistic research in Turkish and for wider cross-linguistic comparisons are discussed.

Wordnets are large-scale lexical databases of related words and concepts, useful for language-aware software applications. They have recently been built for many languages by using various approaches. The Finnish wordnet, FinnWordNet (FiWN), was created by translating the more than 200,000 word senses in the English Princeton WordNet (PWN) 3.0 in 100 days. To ensure quality, they were translated by professional translators. The direct translation approach was based on the assumption that most synsets in PWN represent language-independent real-world concepts. Thus also the semantic relations between synsets were assumed mostly language-independent, so the structure of PWN could be reused as well. This approach allowed the creation of an extensive Finnish wordnet directly aligned with PWN and also provided us with a translation relation and thus a bilingual wordnet usable as a dictionary. In this paper, we address several concerns raised with regard to our approach, many of them for the first time. We evaluate the craftsmanship of the translators by checking the spelling and translation quality, the viability of the approach by assessing the synonym quality both on the lexeme and concept level, as well as the usefulness of the resulting lexical resource both for humans and in a language-technological task. We discovered no new problems compared with those already known in PWN. As a whole, the paper contributes to the scientific discourse on what it takes to create a very large wordnet. As a side-effect of the evaluation, we extended FiWN to contain 208,645 word senses in 120,449 synsets, effectively making version 2.0 of FiWN currently the largest wordnet in the world by these statistics.

Cognitive theories in visual attention and perception, categorization, and memory often critically rely on concepts of similarity among objects, and empirically require measures of “sameness” among their stimuli. For instance, a researcher may require similarity estimates among multiple exemplars of a target category in visual search, or targets and lures in recognition memory. Quantifying similarity, however, is challenging when everyday items are the desired stimulus set, particularly when researchers require several different pictures from the same category. In this article, we document a new multidimensional scaling database with similarity ratings for 240 categories, each containing color photographs of 16–17 exemplar objects. We collected similarity ratings using the spatial arrangement method. Reports include: the multidimensional scaling solutions for each category, up to five dimensions, stress and fit measures, coordinate locations for each stimulus, and two new classifications. For each picture, we categorized the item's prototypicality, indexed by its proximity to other items in the space. We also classified pairs of images along a continuum of similarity, by assessing the overall arrangement of each MDS space. These similarity ratings will be useful to any researcher that wishes to control the similarity of experimental stimuli according to an objective quantification of “sameness.”

Researchers have only recently started to take advantage of the developments in technology and communication for sharing data and documents. However, the exchange of experimental material has not taken advantage of this progress yet. In order to facilitate access to experimental material, the Bank of Standardized Stimuli (BOSS) project was created as a free standardized set of visual stimuli accessible to all researchers, through a normative database. The BOSS is currently the largest existing photo bank providing norms for more than 15 dimensions (e.g. familiarity, visual complexity, manipulability, etc.), making the BOSS an extremely useful research tool and a mean to homogenize scientific data worldwide. The first phase of the BOSS was completed in 2010, and contained 538 normative photos. The second phase of the BOSS project presented in this article, builds on the previous phase by adding 930 new normative photo stimuli. New categories of concepts were introduced, including animals, building infrastructures, body parts, and vehicles and the number of photos in other categories was increased. All new photos of the BOSS were normalized relative to their name, familiarity, visual complexity, object agreement, viewpoint agreement, and manipulability. The availability of these norms is a precious asset that should be considered for characterizing the stimuli as a function of the requirements of research and for controlling for potential confounding effects.

Given the importance of lexical frequency for psycholinguistic research and the lack of comprehensive frequency data for sign languages, we collected subjective estimates of lexical frequency for 432 signs in American Sign Language (ASL). Our participants were 59 deaf signers who first began to acquire ASL at ages ranging from birth to 14 years old and who had a minimum of 10 years of experience. Subjective frequency estimates were made on a scale ranging from 1 = rarely see the sign to 7 = always see the sign. The mean subjective frequency ratings for individual signs did not vary in relation to age of sign language exposure (AoLE), chronological age, or length of ASL experience. Nor did AoLE show significant effects on the response times (RTs) for making the ratings. However, RTs were highly correlated with mean frequency ratings. These results suggest that the distributions of subjective lexical frequencies are consistent across signers with varying AoLEs. The implications for research practice are that subjective frequency ratings from random samples of highly experienced deaf signers can provide a reasonable measure of lexical control in sign language experiments. The Appendix gives the mean and median subjective frequency ratings and the mean and median log(RT) of the ASL signs for the entire sample; the supplemental material gives these measures for the three AoLE groups: native, early, and late.

Short Message Service (SMS) messages are short messages sent from one person to another from their mobile phones. They represent a means of personal communication that is an important communicative artifact in our current digital era. As most existing studies have used private access to SMS corpora, comparative studies using the same raw SMS data have not been possible up to now. We describe our efforts to collect a public SMS corpus to address this problem. We use a battery of methodologies to collect the corpus, paying particular attention to privacy issues to address contributors’ concerns. Our live project collects new SMS message submissions, checks their quality, and adds valid messages. We release the resultant corpus as XML and as SQL dumps, along with monthly corpus statistics. We opportunistically collect as much metadata about the messages and their senders as possible, so as to enable different types of analyses. To date, we have collected more than 71,000 messages, focusing on English and Mandarin Chinese.

The impact-es diachronic corpus of historical Spanish compiles over one hundred books—containing approximately 8 million words—in addition to a complementary lexicon which links more than 10,000 lemmas with attestations of the different variants found in the documents. This textual corpus and the accompanying lexicon have been released under an open license (Creative Commons by-nc-sa) in order to permit their intensive exploitation in linguistic research. Approximately 7 % of the words in the corpus (a selection aimed at enhancing the coverage of the most frequent word forms) have been annotated with their lemma, part of speech, and modern equivalent. This paper describes the annotation criteria followed and the standards, based on the Text Encoding Initiative recommendations, used to represent the texts in digital form.

This article describes the creation and application of the Turk Bootstrap Word Sense Inventory for 397 frequent nouns, which is a publicly available resource for lexical substitution. This resource was acquired using Amazon Mechanical Turk. In a bootstrapping process with massive collaborative input, substitutions for target words in context are elicited and clustered by sense; then, more contexts are collected. Contexts that cannot be assigned to a current target word’s sense inventory re-enter the bootstrapping loop and get a supply of substitutions. This process yields a sense inventory with its granularity determined by substitutions as opposed to psychologically motivated concepts. It comes with a large number of sense-annotated target word contexts. Evaluation on data quality shows that the process is robust against noise from the crowd, produces a less fine-grained inventory than WordNet and provides a rich body of high precision substitution data at low cost. Using the data to train a system for lexical substitutions, we show that amount and quality of the data is sufficient for producing high quality substitutions automatically. In this system, co-occurrence cluster features are employed as a means to cheaply model topicality.

Semantic role labeling is traditionally viewed as a sentence-level task concerned with identifying semantic arguments that are overtly realized in a fairly local context (i.e., a clause or sentence). However, this local view potentially misses important information that can only be recovered if local argument structures are linked across sentence boundaries. One important link concerns semantic arguments that remain locally unrealized (null instantiations) but can be inferred from the context. In this paper, we report on the SemEval 2010 Task-10 on “Linking Events and Their Participants in Discourse”, that addressed this problem. We discuss the corpus that was created for this task, which contains annotations on multiple levels: predicate argument structure (FrameNet and PropBank), null instantiations, and coreference. We also provide an analysis of the task and its difficulties.

A widely agreed-upon feature of spoken word recognition is that multiple lexical candidates in memory are simultaneously activated in parallel when a listener hears a word, and that those candidates compete for recognition (Luce, Goldinger, Auer, {\&} Vitevitch, Perception 62:615-625, 2000; Luce {\&} Pisoni, Ear and Hearing 19:1-36, 1998; McClelland {\&} Elman, Cognitive Psychology 18:1-86, 1986). Because the presence of those competitors influences word recognition, much research has sought to quantify the processes of lexical competition. Metrics that quantify lexical competition continuously are more effective predictors of auditory and visual (lipread) spoken word recognition than are the categorical metrics traditionally used (Feld {\&} Sommers, Speech Communication 53:220-228, 2011; Strand {\&} Sommers, Journal of the Acoustical Society of America 130:1663-1672, 2011). A limitation of the continuous metrics is that they are somewhat computationally cumbersome and require access to existing speech databases. This article describes the Phi-square Lexical Competition Database (Phi-Lex): an online, searchable database that provides access to multiple metrics of auditory and visual (lipread) lexical competition for English words, available at www.juliastrand.com/phi-lex .

A corpus of 5,765 consonant-vowel-consonant sequences (CVCs) was compiled, and phonotactic probability and neighborhood density were computed for both child and adult corpora. This corpus of CVCs, provided as supplementary materials, was analyzed to address the following questions: (1) Do computations based on a child corpus differ from those based on an adult corpus? (2) Do the phonotactic probability and/or the neighborhood density of real words differ from those of nonwords? (3) Do phonotactic probability and/or neighborhood density differ across CVCs that vary in consonant age of acquisition? The results showed significant differences in phonotactic probability and neighborhood density for the child versus adult corpora, replicating prior findings. The impact of this difference on future studies will depend on the level of precision needed when specifying probability and density. In addition, significant and large differences in phonotactic probability and neighborhood density were detected between real words and nonwords, which may present methodological challenges for future research. Finally, CVCs composed of earlier-acquired sounds differed significantly in probability and density from those composed of later-acquired sounds, although this effect was relatively small and is less likely to present significant methodological challenges to future studies.

This paper introduces the first version of the Arabic Learner Corpus (ALC), which comprises a collection of texts written by learners of Arabic in Saudi Arabia. The corpus covers two types of students, non-native Arabic speakers (NNAS) learning Arabic as a second language (ASL) for academic purpose (AAP), and native Arabic speaking students (NAS) learning to improve their written Arabic. Both groups are males at pre- university level.

Despite their relatively low sampling factor, the freely available, randomly sampled status streams of Twitter are very useful sources of geographically embedded social network data. To statistically analyze the information Twitter provides via these streams, we have collected a year's worth of data and built a multi-terabyte relational database from it. The database is designed for fast data loading and to support a wide range of studies focusing on the statistics and geographic features of social networks, as well as on the linguistic analysis of tweets. In this paper we present the method of data collection, the database design, the data loading procedure and special treatment of geo-tagged and multi-lingual data. We also provide some SQL recipes for computing network statistics.

This paper describes the Atlante Sintattico d'Italia, Syntactic Atlas of Italy (ASIt) linguistic linked dataset. ASIt is a scientific project aiming to account for minimally different variants within a sample of closely related languages; it is part of the Edisyn network, the goal of which is to establish a European network of researchers in the area of language syntax that use similar standards with respect to methodology of data collection, data storage and annotation, data retrieval and cartography. In this context, ASIt is defined as a curated database which builds on dialectal data gathered during a twenty-year-long survey investigating the distribution of several grammatical phenomena across the dialects of Italy. Both the ASIt linguistic linked dataset and the Resource Description Framework Schema (RDF/S) on which it is based are publicly available and released with a Creative Commons license (CC BY-NC-SA 3.0). We report the characteristics of the data exposed by ASIt, the statistics about the evolution of the data in the last two years, and the possible usages of the dataset, such as the generation of linguistic maps. {\textcopyright} 2012 - IOS Press and the authors. All rights reserved.

The processes involved in past tense verb generation have been central to models of inflectional morphology. However, the empirical support for such models has often been based on studies of accuracy in past tense verb formation on a relatively small set of items. We present the first large-scale study of past tense inflection (the Past Tense Inflection Project, or PTIP) that affords response time, accuracy, and error analyses in the generation of the past tense form from the present tense form for over 2,000 verbs. In addition to standard lexical variables (such as word frequency, length, and orthographic and phonological neighborhood), we have also developed new measures of past tense neighborhood consistency and verb imageability for these stimuli, and via regression analyses we demonstrate the utility of these new measures in predicting past tense verb generation. The PTIP can be used to further evaluate existing models, to provide well controlled stimuli for new studies, and to uncover novel theoretical principles in past tense morphology.

The Nelson and Narens (Journal of Verbal Learning and Verbal Behavior 19:338-368, 1980) general knowledge norms have been valuable to researchers in many fields. However, much has changed over the 32 years since the 1980 norms. For example, in 1980, most people knew the answer to the question "What is the name of the Lone Ranger's Indian sidekick?" (answer: Tonto), whereas in 2012, few people know this answer. Thus, we updated the 1980 norms and expanded them by providing new measures. In particular, we report two new metacognitive measures (confidence judgments and peer judgments) and provide a detailed report of commission errors. Each of these measures will be valuable to researchers, and together they are likely to facilitate future research in a number of fields, such as research investigating memory illusions, metamemory processes, and error correction. The presence of substantial generational shifts from 1980 to 2012 necessitates the use of updated norms.

In this study, we report normative data by native Persian speakers for concept familiarity, age of acquisition (AoA), imageability, image agreement, name agreement, and visual complexity, as well as values for word frequency, word length, and naming latency for 200 of the colored Snodgrass and Vanderwart (Journal of Experimental Psy- chology: Human Learning and Memory 6:174-215, 1980) pictures created by Rossion and Pourtois (Perception 33:217-236, 2004). Using multiple regression analysis, we found independent effects of name agreement, image agree- ment, word frequency, and AoA on picture naming by native Persian speakers from Iran. We concluded that the psycholinguistic properties identified in studies of picture naming in many other languages also predict timed picture naming in Persian. Normativedatafor theratings and picture-naming latencies for the 200 Persian object nouns are provided as an Excel file in the Supplemental materials.

Nonverbal vocal expressions, such as laughter, sobbing, and screams, are an important source of emotional information in social interactions. However, the investigation of how we process these vocal cues entered the research agenda only recently. Here, we introduce a new corpus of nonverbal vocalizations, which we recorded and submitted to perceptual and acoustic validation. It consists of 121 sounds expressing four positive emotions (achievement/triumph, amusement, sensual pleasure, and relief) and four negative ones (anger, disgust, fear, and sadness), produced by two female and two male speakers. For perceptual validation, a forced choice task was used (n = 20), and ratings were collected for the eight emotions, valence, arousal, and authenticity (n = 20). We provide these data, detailed for each vocalization, for use by the research community. High recognition accuracy was found for all emotions (86 {\%}, on average), and the sounds were reliably rated as communicating the intended expressions. The vocalizations were measured for acoustic cues related to temporal aspects, intensity, fundamental frequency (f0), and voice quality. These cues alone provide sufficient information to discriminate between emotion categories, as indicated by statistical classification procedures; they are also predictors of listeners' emotion ratings, as indicated by multiple regression analyses. This set of stimuli seems a valuable addition to currently available expression corpora for research on emotion processing. It is suitable for behavioral and neuroscience research and might as well be used in clinical settings for the assessment of neurological and psychiatric patients. The corpus can be downloaded from Supplementary Materials.

The temporal characteristics of speech can be captured by examining the distributions of the durations of measurable speech components, namely speech segment durations and pause durations. However, several barriers prevent the easy analysis of pause durations: The first problem is that natural speech is noisy, and although recording contrived speech minimizes this problem, it also discards diagnostic information about cognitive processes inherent in the longer pauses associated with natural speech. The second issue concerns setting the distribution threshold, and consists of the problem of appropriately classifying pause segments as either short pauses reflecting articulation or long pauses reflecting cognitive processing, while minimizing the overall classification error rate. This article describes a fully automated system for determining the locations of speech-pause transitions and estimating the temporal parameters of both speech and pause distributions in natural speech. We use the properties of Gaussian mixture models at several stages of the analysis, in order to identify theoretical components of the data distributions, to classify speech components, to compute durations, and to calculate the relevant statistics.;

The Writing Pal is an intelligent tutoring system that provides writing strategy training. A large part of its artificial intelligence resides in the natural language processing algorithms to assess essay quality and guide feedback to students. Because writing is often highly nuanced and subjective, the development of these algorithms must consider a broad array of linguistic, rhetorical, and contextual features. This study assesses the potential for computational indices to predict human ratings of essay quality. Past studies have demonstrated that linguistic indices related to lexical diversity, word frequency, and syntactic complexity are significant predictors of human judgments of essay quality but that indices of cohesion are not. The present study extends prior work by including a larger data sample and an expanded set of indices to assess new lexical, syntactic, cohesion, rhetorical, and reading ease indices. Three models were assessed. The model reported by McNamara, Crossley, and McCarthy (Written Communication 27:57-86, 2010) including three indices of lexical diversity, word frequency, and syntactic complexity accounted for only 6{\%} of the variance in the larger data set. A regression model including the full set of indices examined in prior studies of writing predicted 38{\%} of the variance in human scores of essay quality with 91{\%} adjacent accuracy (i.e., within 1 point). A regression model that also included new indices related to rhetoric and cohesion predicted 44{\%} of the variance with 94{\%} adjacent accuracy. The new indices increased accuracy but, more importantly, afford the means to provide more meaningful feedback in the context of a writing tutoring system.

An increasing number of studies are investigating the cognitive processes underlying human-object interactions. For instance, several researchers have manipulated the type of grip associated with objects in order to study the role of the objects' motor affordances in cognition. The objective of the present study was to develop norms for the types of grip employed when grasping and using objects, with a set of 296 photographs of objects. On the basis of these ratings, we computed measures of agreement to evaluate the extent to which participants agreed about the grip used to interact with these objects. We also collected ratings on the dissimilarity between the grips employed for grasping and for using objects, as well as the number of actions that can typically be performed with the objects. Our results showed grip agreements of 67 {\%} for grasping and of 65 {\%} for using objects. Moreover, our pattern of correlations is highly consistent with the idea that the grips for grasping and using objects represent two different motor dimensions of the objects.

In this article, we describe the most extensive set of word associations collected to date. The database contains over 12,000 cue words for which more than 70,000 participants generated three responses in a multiple-response free association task. The goal of this study was (1) to create a semantic network that covers a large part of the human lexicon, (2) to investigate the implications of a multiple-response procedure by deriving a weighted directed network, and (3) to show how measures of centrality and relatedness derived from this network predict both lexical access in a lexical decision task and semantic relatedness in similarity judgment tasks. First, our results show that the multiple-response procedure results in a more heterogeneous set of responses, which lead to better predictions of lexical access and semantic relatedness than do single-response procedures. Second, the directed nature of the network leads to a decomposition of centrality that primarily depends on the number of incoming links or in-degree of each node, rather than its set size or number of outgoing links. Both studies indicate that adequate representation formats and sufficiently rich data derived from word associations represent a valuable type of information in both lexical and semantic processing.

The combining of individual concepts to form an emergent concept is a fundamental aspect of language, yet much less is known about it than about processing isolated words or sentences. To facilitate research on conceptual combination, we provide meaningfulness ratings for a large set of (2,160) noun-noun pairs. Half of these pairs (1,080) are reversed versions of the other half (e.g., SKI JACKET and JACKET SKI), to facilitate the comparison of successful and unsuccessful conceptual combination independently of constituent lexical items. The computer code used for obtaining these ratings through a Web interface is provided. To further enhance the usefulness of this resource, ancillary measures obtained from other sources are also provided for each pair. These measures include associate production norms, contextual relatedness in terms of latent semantic analysis distance, total number of letters, phrase-level usage frequency, and word-level usage frequency summed across the words in each pair. Results of correlation and regression analyses are also provided for a quantitative description of the stimulus set. A subset of these stimuli was used to identify neural correlates of successful conceptual combination Graves, Binder, Desai, Conant, {\&} Seidenberg, (NeuroImage 53:638-646, 2010). The stimuli can be used in other research and also provide benchmark data for evaluating the effectiveness of computational algorithms for predicting meaningfulness of noun-noun pairs.

The aim of the present study was to provide normative data for the Croatian language using 346 visually presented objects (Cycowicz, Friedman, Rothstein, {\&} Snodgrass Journal of Experimental Child Psychology 65:171-237, 1997; Roach, Schwartz, Martin, Grewal, {\&} Brecher Clinical Aphasiology 24:121-133, 1996; Snodgrass {\&} Vanderwart Journal of Experimental Psychology: Human Learning and Memory 6:174-215, 1980). Picture naming was standardized according to seven variables: naming latency, name agreement, familiarity, visual complexity, word length, number of syllables, and word frequency. The descriptive statistics and correlation pattern of the variables collected in the present study were consistent with normative studies in other languages. These normative data for pictorial stimuli named by young healthy Croatian native speakers will be useful in studies of perception, language, and memory, as well as for preoperative and intraoperative mapping of speech and language brain areas.

Indicators of letter visual similarity have been used for controlling the design of empirical and neuropsychological studies and for rigorously determining the factors that underlie reading ability and literacy acquisition. Additionally, these letter similarity/confusability matrices have been useful for studies examining more general aspects of human cognition, such as perception. Despite many letter visual-similarity matrices being available, they all have two serious limitations if they are to be used by researchers in the reading domain: (1) They have been constructed using atypical reading data obtained from speeded reading-aloud tasks and/or under degraded presentation conditions; (2) they only include letters from the English alphabet. Although some letter visual-similarity matrices have been constructed using data gathered from normal reading conditions, these either are based on old fonts, which may not resemble the letters found in modern print, or were never published. For the first time, this article presents a comprehensive letter visual-similarity/confusability matrix that has been constructed based on untimed responses to clearly presented upper- and lowercase letters that are present in many languages that use Latin-based alphabets, including Catalan, Dutch, English, French, Galician, German, Italian, Portuguese, and Spanish. Such a matrix will be useful for researchers interested in the processes underpinning reading and literacy acquisition.

The substantial increase of social networks and their combination with mobile devices make rigorous analysis of the outcomes of such system of paramount importance for intelligence gathering and decision making purposes. Since the introduction of Twitter system in 2006, tweeting emerged as an efficient open social network that attracted interest from various research/commercial and military communities. This paper investigates the current software architecture of Twitter system and put forward a new architecture dedicated for semantic and spatial analysis of Twitter data. Especially, Twitter Streaming API was used as a basis for tweet collection data stored in MySQL like database. While Lucene system together with WordNet lexical database linked to advanced natural language processing and PostGIS platform were used to ensure semantic and spatial analysis of the collected data. A functional diversity approach was implemented to enforce fault tolerance for the data collection part where its performances were evaluated through comparison with alternative approaches. The proposal enables the discovery of spatial patterns within geo-located Twitter and can provide the user or operator with useful unforeseen elements. {\textcopyright} 2012 Elsevier B.V. All rights reserved.

We present modality exclusivity norms for 400 randomly selected noun concepts, for which participants provided perceptual strength ratings across five sensory modalities (i.e., hearing, taste, touch, smell, and vision). A comparison with previous norms showed that noun concepts are more multimodal than adjective concepts, as nouns tend to subsume multiple adjectival property concepts (e.g., perceptual experience of the concept baby involves auditory, haptic, olfactory, and visual properties, and hence leads to multimodal perceptual strength). To show the value of these norms, we then used them to test a prediction of the sound symbolism hypothesis: Analysis revealed a systematic relationship between strength of perceptual experience in the referent concept and surface word form, such that distinctive perceptual experience tends to attract distinctive lexical labels. In other words, modality-specific norms of perceptual strength are useful for exploring not just the nature of grounded concepts, but also the nature of form-meaning relationships. These norms will be of benefit to those interested in the representational nature of concepts, the roles of perceptual information in word processing and in grounded cognition more generally, and the relationship between form and meaning in language development and evolution.

NIM is Web-based software developed to help experimenters with some of the usual tasks carried out in psycholinguistic studies. It allows the user to search for words according to several variables, such as length, matching substrings, lexical frequency, or part of speech, in English, Spanish, and Catalan. NIM also provides the user with the possibilities to obtain different word metrics, such as lexical frequency, length, and part of speech; to find intralanguage and cross-language lexical neighbors; and to get control words for critical stimuli. Regardless of the language used, the program also enables the user to get the orthographic similarity between word pairs and to identify repeated items in lists of experimental stimuli. NIM is free and is publicly available at http://psico.fcep.urv.cat/utilitats/nim/ .

Sensory experience ratings (SERs) reflect the extent to which a word evokes a sensory and/or perceptual experience in the mind of the reader. Juhasz, Yap, Dicke, Taylor, and Gullick (Quarterly Journal of Experimental Psychology 64:1683-1691, 2011) demonstrated that SERs predict a significant amount of variance in lexical-decision response times in two megastudies of lexical processing when a large number of established psycholinguistic variables are controlled for. Here we provide the SERs for the 2,857 monosyllabic words used in the Juhasz et al. study, as well as newly collected ratings on 3,000 disyllabic words. New analyses with the combined set of words confirmed that SERs predict a reliable amount of variance in the lexical-decision response times and naming times from the English Lexicon Project (Balota, Yap, Cortese, Hutchison, Kessler, Loftus, {\&} Treiman, Behavior Research Methods 39:445-459, 2007) when a large number of surface, lexical, and semantic variables are statistically controlled for. The results suggest that the relative availability of sensory/perceptual information associated with a word contributes to lexical-semantic processing.

Built on the basis of the methods developed for Princeton WordNet and EuroWordNet, Arabic WordNet (AWN) has been an interesting project which combines WordNet structure compliance with Arabic particularities. In this paper, some AWN shortcomings related to coverage and usability are addressed. The use of AWN in question/answering (Q/A) helped us to deeply evaluate the resource from an experience-based perspective. Accordingly, an enrichment of AWN was built by semi-automatically extending its content. Indeed, existing approaches and/or resources developed for other languages were adapted and used for AWN. The experiments conducted in Arabic Q/A have shown an improvement of both AWN coverage as well as usability. Concerning coverage, a great amount of named entities extracted from YAGO were connected with corresponding AWN synsets. Also, a significant number of new verbs and nouns (including Broken Plural forms) were added. In terms of usability, thanks to the use of AWN, the performance for the AWN-based Q/A application registered an overall improvement with respect to the following three measures: accuracy (+9.27 % improvement), mean reciprocal rank (+3.6 improvement) and number of answered questions (+12.79 % improvement).

Since long it has been noted that cross-linguistically recurring polysemies can serve as an indi-cator of conceptual relations, and quite a few approaches to model and analyze such data have been proposed in the recent past. Although – given the nature of the data – it seems natural to model and analyze it with the help of network techniques, there are only a few approaches which make explicit use of them. In this paper, we show how the strict application of weighted network models helps to get more out of cross-linguistic polysemies than would be possible using approaches that are only based on item-to-item comparison. For our study we use a large dataset consisting of 1252 semantic items translated into 195 different languages covering 44 different language families. By analyz-ing the community structure of the network reconstructed from the data, we find that a majority of the concepts (68{\%}) can be separated into 104 large communities consisting of five and more nodes. These large communities almost exclusively constitute meaningful groupings of concepts into con-ceptual fields. They provide a valid starting point for deeper analyses of various topics in historical semantics, such as cognate detection, etymological analysis, and semantic reconstruction.

LAPSyD, the Lyon-Albuquerque Phonological Systems Database, is an online phonological database equipped with powerful query, mapping and visualization tools. It stems from the UPSID and WALS databases, enhanced with newly validated data not only covering segmental inventories but also syllable structures, stress and tonal systems. In its current version it covers around 700 languages and it is accessible at http://www.lapsyd.ddl.ish-lyon.cnrs.fr. This paper provides a description of the data structure in LAPSyD and the features of the interface. Brief illustrations of the types of analysis that can be done with this tool are provided, exploiting the ability to cross-reference data on segments, other phonological properties and language location. Copyright {\textcopyright} 2013 ISCA.

This paper describes the SubCat-Extractor as a novel tool to obtain verb subcategori-sation data from parsed German web corpora. The SubCat-Extractor is based on a set of detailed rules that go beyond what is directly accessible in the parses. The extracted subcategorisation database is represented in a compact but linguistically detailed and flexible format, comprising various aspects of verb information, complement information and sentence information , within a one-line-per-clause style. We describe the tool, the extraction rules and the obtained resource database, as well as actual and potential uses in computational linguistics.

Semantic norms for properties produced by native speakers are valuable tools for researchers interested in the structure of semantic memory and in category-specific semantic deficits in individuals following brain damage. The aims of this study were threefold. First, we sought to extend existing semantic norms by adopting an empirical approach to category (Exp. 1) and concept (Exp. 2) selection, in order to obtain a more representative set of semantic memory features. Second, we extensively outlined a new set of semantic production norms collected from Italian native speakers for 120 artifactual and natural basic-level concepts, using numerous measures and statistics following a feature-listing task (Exp. 3b). Finally, we aimed to create a new publicly accessible database, since only a few existing databases are publicly available online.

We make available word-by-word self-paced reading times and eye-tracking data over a sample of English sentences from narrative sources. These data are intended to form a gold standard for the evaluation of computational psycholinguistic models of sentence comprehension in English. We describe stimuli selection and data collection and present descriptive statistics, as well as comparisons between the two sets of reading times.

As researchers explore the complexity of memory and language hierarchies, the need to expand normed stimulus databases is growing. Therefore, we present 1,808 words, paired with their features and concept-concept information, that were collected using previously established norming methods (McRae, Cree, Seidenberg, {\&} McNorgan Behavior Research Methods 37:547-559, 2005). This database supplements existing stimuli and complements the Semantic Priming Project (Hutchison, Balota, Cortese, Neely, Niemeyer, Bengson, {\&} Cohen-Shikora 2010). The data set includes many types of words (including nouns, verbs, adjectives, etc.), expanding on previous collections of nouns and verbs (Vinson {\&} Vigliocco Journal of Neurolinguistics 15:317-351, 2008). We describe the relation between our and other semantic norms, as well as giving a short review of word-pair norms. The stimuli are provided in conjunction with a searchable Web portal that allows researchers to create a set of experimental stimuli without prior programming knowledge. When researchers use this new database in tandem with previous norming efforts, precise stimuli sets can be created for future research endeavors.

Extraction and normalization of temporal expressions from documents are important steps towards deep text understanding and a prerequisite for many NLP tasks such as information extraction, question answering, and document summarization. There are different ways to express (the same) temporal information in documents. However, after identifying temporal expressions, they can be normalized according to some standard format. This allows the usage of temporal information in a term- and language-independent way. In this paper, we describe the challenges of temporal tagging in different domains, give an overview of existing annotated corpora, and survey existing approaches for temporal tagging. Finally, we present our publicly available temporal tagger HeidelTime, which is easily extensible to further languages due to its strict separation of source code and language resources like patterns and rules. We present a broad evaluation on multiple languages and domains on existing corpora as well as on a newly created corpus for a language/domain combination for which no annotated corpus has been available so far.

Literature review on prosody reveals the lack of corpora for prosodic studies in Catalan and Spanish. In this paper, we present a corpus intended to fill this gap. The corpus comprises two distinct data-sets, a news subcorpus and a dialogue subcorpus, the latter containing either conversational or task-oriented speech. More than 25 h were recorded by twenty eight speakers per language. Among these speakers, eight were professional (four radio news broadcasters and four advertising actors). The entire material presented here has been transcribed, aligned with the acoustic signal and prosodically annotated. Two major objectives have guided the design of this project: (i) to offer a wide coverage of representative real-life communicative situations which allow for the characterization of prosody in these two languages; and (ii) to conduct research studies which enable us to contrast the speakers different speaking styles and discursive practices. All material contained in the corpus is provided under a Creative Commons Attribution 3.0 Unported License.

In cognitive science, results are obtained following the manipulation of one stimulus' variable and the control of potential confounding variables. To avoid the tedious task of measuring confounding effects, scientists often refer to normative sets of stimuli. The Bank of Standardized Stimuli (BOSS) is one of these sets. It initially included 480 normative stimuli of common objects and norms for seven variables (name, category, familiarity, visual complexity, object's typicality, manipulability and orientation). The BOSS has expanded and provides a wider variety of stimuli in order to fulfill the needs of experiments. To date, the latest version of the BOSS is comprised of 1,420 normative stimuli, including photos of animals, and new norms (color diagnositicity, symmetry, and action related norms). In order to demonstrate the influence of normative variables on cognitions, experiments on episodic memory were completed using the BOSS. Analyses were conducted as a function of the norms and indicated that name agreement, visual complexity, object/viewpoint agreement, symmetry, and color diagnosticity all influenced memory in distinct ways mostly by affecting the performance to new stimuli and by inducing response biases.

We report object-naming and object recognition times collected from Russian native speakers for the colorized version of the Snodgrass and Vanderwart (Journal of Experimental Psychology: Human Learning and Memory 6:174-215, 1980) pictures (Rossion {\&} Pourtois, Perception 33:217-236, 2004). New norms for image variability, body-object interaction [BOI], and subjective frequency collected in Russian, as well as new name agreement scores for the colorized pictures in French, are also reported. In both object-naming and object comprehension times, the name agreement, image agreement, and age-of-acquisition variables made significant independent contributions. Objective word frequency was reliable in object-naming latencies only. The variables of image variability, BOI, and subjective frequency were not significant in either object naming or object comprehension. Finally, imageability was reliable in both tasks. The new norms and object-naming and object recognition times are provided as supplemental materials.

Although many visual stimulus databases exist, none has data on item similarity levels for multiple items of each kind of stimulus. We present such data for 50 sets of grayscale object photographs. Similarity measures between pictures in each set (e.g., 25 different buttons) were collected using a similarity-sorting method (Goldstone, Behavior Research Methods Instruments {\&} Computers, 26(4):381-386, 1994). A validation experiment used data from 1 picture set and compared responses from standard pairwise measures. This showed close agreement. The similarity-sorting measures were then standardized across picture sets, using pairwise ratings. Finally, the standardized similarity distances were validated in a recognition memory experiment; false alarms increased when targets and foils were more similar. These data will facilitate memory and perception research that needs to make comparisons between stimuli with a range of known target-foil similarities.

We present a corpus of transcribed spoken Hebrew that reflects spoken interactions between children and adults. The corpus is an integral part of the CHILDES database, which distributes similar corpora for over 25 languages. We introduce a dedicated transcription scheme for the spoken Hebrew data that is sensitive to both the phonology and the standard orthography of the language. We also introduce a morphological analyzer that was specifically developed for this corpus. The analyzer adequately covers the entire corpus, producing detailed correct analyses for all tokens. Evaluation on a new corpus reveals high coverage as well. Finally, we describe a morphological disambiguation module that selects the correct analysis of each token in context. The result is a high-quality morphologically-annotated CHILDES corpus of Hebrew, along with a set of tools that can be applied to new corpora.

Social media is a natural laboratory for linguistic and sociological purposes. In micro-blogging platforms such as Twitter, people share hundreds of millions of short messages about their lives and experiences on a daily basis. These messages, coupled with metadata about their authors, provide an opportunity to understand a wide variety of phenomena ranging from political polarization to geographic and demographic lexical variation. Lack of publicly available micro-blogging datasets has been a hindrance to replicable research. In this paper, I introduce Rovereto Twitter n-gram corpus, a publicly available n-gram dataset of Twitter messages, which contains gender-of-the-author and time-of-posting tags associated with the n-grams. I compare this dataset to a more traditional web-based corpus and present a case study which shows the potential of combining an n-gram corpus with demographic metadata.

We introduce the ACL Anthology Network (AAN), a comprehensive manually curated networked database of citations, collaborations, and summaries in the field of Computational Linguistics. We also present a number of statistics about the network including the most cited authors, the most central collaborators, as well as network statistics about the paper citation, author citation, and author collaboration networks.

The project on the Romanian wordnet has been under continuous development for more than 10 years now. It has been in constant use in many projects and applications which determined, to a large extent, the content and coverage of various lexical domains. The article presents the most recent developments of the Romanian wordnet and offers quantitative data for its current version.

In this study, we present the normative values of the adaptation of the International Affective Digitized Sounds (IADS-2; Bradley {\&} Lang, 2007a) for European Portuguese (EP). The IADS-2 is a standardized database of 167 naturally occurring sounds that is widely used in the study of emotions. The sounds were rated by 300 college students who were native speakers of EP, in the three affective dimensions of valence, arousal, and dominance, by using the Self-Assessment Manikin (SAM). The aims of this adaptation were threefold: (1) to provide researchers with standardized and normatively rated affective sounds to be used with an EP population; (2) to investigate sex and cultural differences in the ratings of affective dimensions of auditory stimuli between EP and the American (Bradley {\&} Lang, 2007a) and Spanish (Fern{\'{a}}ndez-Abascal et al., Psicothema 20:104-113 2008; Redondo, Fraga, Padr{\'{o}}n, {\&} Pi{\~{n}}eiro, Behavior Research Methods 40:784-790 2008) standardizations; and (3) to promote research on auditory affective processing in Portugal. Our results indicated that the IADS-2 is a valid and useful database of digitized sounds for the study of emotions in a Portuguese context, allowing for comparisons of its results with those of other international studies that have used the same database for stimulus selection. The normative values of the EP adaptation of the IADS-2 database can be downloaded along with the online version of this article.

We report psycholinguistic norms for 305 French idiomatic expressions (Study 1). For each of the idiomatic expressions, the following variables are reported: knowledge, predictability, literality, compositionality, subjective and objective frequency, familiarity, age of acquisition (AoA), and length. In addition, we have collected comprehension times for each idiom (Study 2). The psycholinguistic relevance of the collected norms is explained, and different analyses (descriptive statistics, correlation and multiple regression analyses) performed on the norms are reported and discussed. The entire set of norms and reading times are provided as supplemental material.

Feature-based descriptions of concepts produced by subjects in a property generation task are widely used in cognitive science to develop empirically grounded concept representations and to study systematic trends in such representations. This article introduces BLIND, a collection of parallel semantic norms collected from a group of congenitally blind Italian subjects and comparable sighted subjects. The BLIND norms comprise descriptions of 50 nouns and 20 verbs. All the materials have been semantically annotated and translated into English, to make them easily accessible to the scientific community. The article also presents a preliminary analysis of the BLIND data that highlights both the large degree of overlap between the groups and interesting differences. The complete BLIND norms are freely available and can be downloaded from http://sesia.humnet.unipi.it/blind{\_}data .

This article introduces EsPal: a Web-accessible repository containing a comprehensive set of properties of Spanish words. EsPal is based on an extensible set of data sources, beginning with a 300 million token written database and a 460 million token subtitle database. Properties available include word frequency, orthographic structure and neighborhoods, phonological structure and neighborhoods, and subjective ratings such as imageability. Subword structure properties are also available in terms of bigrams and trigrams, biphones, and bisyllables. Lemma and part-of-speech information and their corresponding frequencies are also indexed. The website enables users either to upload a set of words to receive their properties or to receive a set of words matching constraints on the properties. The properties themselves are easily extensible and will be added over time as they become available. It is freely available from the following website: http://www.bcbl.eu/databases/espal/ .

Speeded naming and lexical decision data for 1,661 target words following related and unrelated primes were collected from 768 subjects across four different universities. These behavioral measures have been integrated with demographic information for each subject and descriptive characteristics for every item. Subjects also completed portions of the Woodcock-Johnson reading battery, three attentional control tasks, and a circadian rhythm measure. These data are available at a user-friendly Internet-based repository ( http://spp.montana.edu ). This Web site includes a search engine designed to generate lists of prime-target pairs with specific characteristics (e.g., length, frequency, associative strength, latent semantic similarity, priming effect in standardized and raw reaction times). We illustrate the types of questions that can be addressed via the Semantic Priming Project. These data represent the largest behavioral database on semantic priming and are available to researchers to aid in selecting stimuli, testing theories, and reducing potential confounds in their studies.

This paper provides a deduction-based approach for automatically classifying compound-internal relations in GermaNet, the German version of the Princeton WordNet for English. More specifically, meronymic relations between simplex and compound nouns provide the necessary input to the deduction patterns that involve different types of compound-internal relations. The scope of these deductions extends to all four meronymic relations modeled in version 6.0 of GermaNet: component, member, substance, and portion. This deduction-based approach provides an effective method for automatically enriching the set of semantic relations included in GermaNet.

The English-language Princeton WordNet (PWN) and some wordnets for other languages have been extensively used as lexical–semantic knowledge sources in language technology applications, due to their free availability and their size. The ubiquitousness of PWN-type wordnets tends to overshadow the fact that they represent one out of many possible choices for structuring a lexical–semantic resource, and it could be enlightening to look at a differently structured resource both from the point of view of theoretical–methodological considerations and from the point of view of practical text processing requirements. The resource described here—SALDO—is such a lexical–semantic resource, intended primarily for use in language technology applications, and offering an alternative organization to PWN-style wordnets. We present our work on SALDO, compare it with PWN, and discuss some implications of the differences. We also describe an integrated infrastructure for computational lexical resources where SALDO forms the central component.

The aim of this article is to describe a database of diphone positional frequencies in French. More specifically, we provide frequencies for word-initial, word-internal, and word-final diphones of all words extracted from a subtitle corpus of 50 million words that come from movie and TV series dialogue. We also provide intra- and intersyllable diphone frequencies, as well as interword diphone frequencies. To our knowledge, no other such tool is available to psycholinguists for the study of French sequential probabilities. This database and its new indicators should help researchers conducting new studies on speech segmentation.

Information about the affective meanings of words is used by researchers working on emotions and moods, word recognition and memory, and text-based sentiment analysis. Three components of emotions are traditionally distinguished: valence (the pleasantness of a stimulus), arousal (the intensity of emotion provoked by a stimulus), and dominance (the degree of control exerted by a stimulus). Thus far, nearly all research has been based on the ANEW norms collected by Bradley and Lang (1999) for 1,034 words. We extended that database to nearly 14,000 English lemmas, providing researchers with a much richer source of information, including gender, age, and educational differences in emotion norms. As an example of the new possibilities, we included stimuli from nearly all of the category norms (e.g., types of diseases, occupations, and taboo words) collected by Van Overschelde, Rawson, and Dunlosky (Journal of Memory and Language 50:289-335, 2004), making it possible to include affect in studies of semantic memory.

The MoveOn speech and noise database was purposely designed and implemented in support of research on spoken dialogue interaction in a motorcycle environment. The distinctiveness of the MoveOn database results from the requirements of the application domain—an information support and operational command and control system for the two-wheel police force—and also from the specifics of the adverse open-air acoustic environment. In this article, we first outline the target application, motivating the database design and purpose, and then report on the implementation details. The main challenges related to the choice of equipment, the organization of recording sessions, and some difficulties that were experienced during this effort, are discussed. We offer a detailed account of the database statistics, the suggested data splits in subsets, and discuss results from automatic speech recognition experiments which illustrate the degree of complexity of the operational environment.

Availability of databases is a necessity in the speech processing field. The publically available databases in Arabic language are few. In this paper we describe a rich database for Arabic language. The database is rich in many dimensions: in text, environments, microphone type, number of recording sessions, recording system, the transmission channel, the country of origin, and the mother language. This richness makes the database an important resource for research in Arabic Language processing and very useful in many speech processing tasks, such as speaker recognition, speech recognition, and accent identification. The speakers were speaking in Modern Standard Arabic (MSA).

The Quranic Arabic Corpus (http://corpus.quran.com) is a collaboratively constructed linguistic resource initiated at the University of Leeds, with multiple layers of annotation including part-of-speech tagging, morphological segmentation (Dukes and Habash 2010) and syntactic analysis using dependency grammar (Dukes and Buckwalter 2010). The motivation behind this work is to produce a resource that enables further analysis of the Quran, the 1,400 year-old central religious text of Islam. This project contrasts with other Arabic treebanks by providing a deep linguistic model based on the historical traditional grammar known as i′rāb (إعراب). By adapting this well-known canon of Quranic grammar into a familiar tagset, it is possible to encourage online annotation by Arabic linguists and Quranic experts. This article presents a new approach to linguistic annotation of an Arabic corpus: online supervised collaboration using a multi-stage approach. The different stages include automatic rule-based tagging, initial manual verification, and online supervised collaborative proofreading. A popular website attracting thousands of visitors per day, the Quranic Arabic Corpus has approximately 100 unpaid volunteer annotators each suggesting corrections to existing linguistic tagging. To ensure a high-quality resource, a small number of expert annotators are promoted to a supervisory role, allowing them to review or veto suggestions made by other collaborators. The Quran also benefits from a large body of existing historical grammatical analysis, which may be leveraged during this review. In this paper we evaluate and report on the effectiveness of the chosen annotation methodology. We also discuss the unique challenges of annotating Quranic Arabic online and describe the custom linguistic software used to aid collaborative annotation.

In recent years, building reference speech corpora was an important part of the activities which provided the necessary linguistic infrastructure in many European countries, for languages with many speakers (e.g., French, German, Spanish, Italian) as well as for those with smaller numbers of speakers (e.g., Swedish, Dutch, Czech, Slovak). This paper describes the process of the creation of a reference speech corpus and its distribution to potential users, as it was done in the case of the Slovene corpus GOS. The corpus structure and fieldwork experiences with recording, labelling system, and two levels of transcription (pronunciation-based and standardized) are described, as well as the main characteristics of the corpus interface (web concordancer) and the availability of the original corpus files.

To cite this version: Pollet Samvelian, Pegah Faghiri. Introducing PersPred, a syntactic and semantic database for Persian Complex Predicates. Abstract This paper introduces PersPred, the first manually elaborated syntactic and semantic database for Persian Complex Predicates (CPs). Beside their theoretical interest, Per-sian CPs constitute an important challenge in Persian lexicography and for NLP. The first delivery, PersPred 1 1 , contains 700 CPs, for which 22 fields of lexical, syntactic and semantic information are encoded. The semantic classification PersPred provides allows to account for the productivity of these combinations in a way which does justice to their compositionality without overlooking their id-iomaticity.

This paper describes the conversion of ItalwordNet and of a domain WordNet into RDF and their linking to the (L)LOD cloud and to other existing resources. A brief presentation of the resources is given, and the conversion and resulting datasets are described.

Normative data on the objective age of acquisition (AoA) for 286 Russian words are presented in this article. In addition, correlations between the objective AoA and subjective ratings, name agreement, picture name agreement, imageability, familiarity, word frequency, and word length are provided, as are correlations between the objective AoA and two measures of exemplar dominance (exemplar generation frequency and the number of times an exemplar was named first). The correlations between the aforementioned variables are generally consistent with the correlations reported in other normative studies. The objective AoA data are highly correlated with the subjective AoA ratings, whereas the correlations between the objective AoA and other psycholinguistic variables are moderate. The correlations between the objective AoA of Russian words and similar data for other languages are moderately high. The complete word norms may be downloaded from supplementary material.

Semantic ambiguity is typically measured by sum-ming the number of senses or dictionary definitions that a word has. Such measures are somewhat subjective and may not adequately capture the full extent of variation in word meaning, particularly for polysemous words that can be used in many different ways, with subtle shifts in meaning. Here, we describe an alternative, computationally derived measure of ambiguity based on the proposal that the meanings of words vary continuously as a function of their contexts. On this view, words that appear in a wide range of contexts on diverse topics are more variable in meaning than those that appear in a restricted set of similar contexts. To quantify this variation, we performed latent semantic analysis on a large text corpus to estimate the semantic similarities of different linguistic contexts. From these estimates, we calculated the degree to which the different contexts associated with a given word vary in their meanings. We term this quantity a word's semantic diversity (SemD). We suggest that this approach provides an objective way of quantifying the subtle, context-dependent variations in word meaning that are often present in language. We demonstrate that SemD is correlated with other measures of ambiguity and contextual variability, as well as with frequency and imageability. We also show that SemD is a strong predictor of performance in semantic judgments in healthy individuals and in patients with semantic deficits, accounting for unique variance beyond that of other predictors. SemD values for over 30,000 English words are provided as supplementary materials.

Newly measured rating norms provide a database of emotion-related dimensions for 524 French trait words. Measures include valence, approach/avoidance tendencies associated with the trait, possessor- and other-relevance of the trait, and discrete emotions conveyed by the trait (i.e., anger, disgust, fear, happiness, and sadness). The normative data were obtained from 328 participants and were revealed to be stable across samples and gender. These data go beyond a dimensional structure and consider more fine-grained descriptions such as the categorical emotions, as well as the perspective of the evaluator conveyed by the traits. They should thus be particularly useful for researchers interested in emotion or in the emotional dimension of cognition, action, or personality. The database is available as supplementary material.

This article presents norms of valence/pleasantness, activity/arousal, power/dominance, and age of acquisition for 4,300 Dutch words, mainly nouns, adjectives, adverbs, and verbs. The norms are based on ratings with a 7-point Likert scale by independent groups of students from two Belgian (Ghent and Leuven) and two Dutch (Rotterdam and Leiden-Amsterdam) samples. For each variable, we obtained high split-half reliabilities within each sample and high correlations between samples. In addition, the valence ratings of a previous, more limited study (Hermans {\&} De Houwer, Psychologica Belgica, 34:115-139, 1994) correlated highly with those of the present study. Therefore, the new norms are a valuable source of information for affective research in the Dutch language.

Stimulus material for studying object-directed actions is needed in different research contexts, such as action observation, action memory, and imitation. Action items have been generated many times in individual laboratories across the world, but they are used in very few experiments. For future studies in the field, it would be worthwhile to have a larger set of action stimulus material available to a broader research community. Some smaller action databases have already been published, but those often focus on psycholinguistic parameters and static action stimuli. With this article, we introduce an action database with dynamic action stimuli. The database contains action descriptions of 1,754 object-directed actions that have been rated for familiarity in Germany and in China. For 784 of these actions, action video clips are available. With the use of our database, it is possible to identify actions that differ in familiarity between Western and Eastern cultures. This variable may be of interest to some researchers in the field, since it has been shown that familiarity influences action information processing. Action descriptions are listed and categorized in tables that can be downloaded, along with the corresponding video clips, as supplemental material.