The Corpus of Contemporary American English is the first large, genre-balanced corpus of any language, which has been designed and constructed from the ground up as a ‘monitor corpus', and which can be used to accurately track and study recent changes in the language. The 400 million words corpus is evenly divided between spoken, fiction, popular magazines, newspapers, and academic journals. Most importantly, the genre balance stays almost exactly the same from year to year, which allows it to accurately model changes in the ‘real world'. After discussing the corpus design, we provide a number of concrete examples of how the corpus can be used to look at recent changes in English, including morphology (new suffixes –friendly and –gate), syntax (including prescriptive rules, quotative like, so not ADJ, the get passive, resultatives, and verb complementation), semantics (such as changes in meaning with web, green, or gay), and lexis––including word and phrase frequency by year, and using the corpus architecture to produce lists of all words that have had large shifts in frequency between specific historical periods.

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.

This paper reports on the TIGER Treebank, a corpus of currently 40,000 syntactically annotated German newspaper sentences. We describe what kind of information is encoded in the treebank and introduce the different representation formats that are used for the annotation and exploitation of the treebank. We explain the different methods used for the annotation: interactive annotation, using the tool ANNOTATE, and LFG parsing. Furthermore, we give an account of the annotation scheme used for the TIGER treebank. This scheme is an extended and improved version of the NEGRA annotation scheme and we illustrate in detail the linguistic extensions that were made concerning the annotation in the TIGER project. The main differences are concerned with coordination, verb-subcategorization, expletives as well as proper nouns. In addition, the paper also presents the query tool TIGERSearch that was developed in the project to exploit the treebank in an adequate way. We describe the query language which was designed to facilitate a simple formulation of complex queries; furthermore, we shortly introduce TIGER in, a graphical user interface for query input. The paper concludes with a summary and some directions for future work.

The York–Toronto–Helsinki Parsed Corpus of Old English Prose (YCOE) is a 1.5 million-word syntactically annotated corpus of Old English prose texts. It was produced at the University of York, UK, between 2000 and 2003, by Ann Taylor, Anthony Warner, Susan Pintzuk and Frank Beths, with a grant from the English Arts and Humanities Research Board (B/RG/AN5907/APN9528). The YCOE is part of the English Parsed Corpora Series. It was the third historical corpus to be completed in this format, and uses the same kind of annotation scheme as its sister corpora, the Penn–Helsinki Parsed Corpus of Middle English II (PPCME2), the York–Helsinki Parsed Corpus of Old English Poetry and the Penn–Helsinki Parsed Corpus of Early Modern English. Two other corpora in the series, the parsed version of the Corpus of Early English Correspondence and the Penn Parsed Corpus of Modern British English, are currently under construction (at the University of York, UK, in cooperation with the University of Helsinki, Finland, and the University of Pennsylvania, USA, respectively).

Developmental differences in name agreement, familiarity, and visual complexity in response to line drawings of common objects were obtained from children and adults. Sixty-one pictures were taken from the Peabody Picture Vocabulary Test-Revised and 259 pictures were taken from the set normed for adults by Snodgrass and Vanderwart (1980). Although there were some differences between the two sets of pictures, the present results replicated the relative independence of these three measures, which was reported by Snodgrass and Vanderwart for adults. Children and adults showed substantial agreement on the names of the pictures. Although the children's ratings were lower on all measures, the differences were trivial for most pictures. We concluded that judgments of familiarity, complexity, and the names of line drawings of common objects are based primarily on information processing accomplished prior to age 7.

According to recent embodied cognition theories, mental concepts are represented by modality-specific sensory-motor systems. Much of the evidence for modality-specificity in conceptual processing comes from the property-verification task. When applying this and other tasks, it is important to select items based on their modality-exclusivity. We collected modality ratings for a set of 387 properties, each of which was paired with two different concepts, yielding a total of 774 concept-property items. For each item, participants rated the degree to which the property could be experienced through five perceptual modalities (vision, audition, touch, smell, and taste). Based on these ratings, we computed a measure of modality exclusivity, the degree to which a property is perceived exclusively through one sensory modality. In this paper, we briefly sketch the theoretical background of conceptual knowledge, discuss the use of the property-verification task in cognitive research, provide our norms and statistics, and validate the norms in a memory experiment. We conclude that our norms are important for researchers studying modality-specific effects in conceptual processing.

[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)

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 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.

Phylogenetic methods have revolutionised evolutionary biology and have recently been applied to studies of linguistic and cultural evolution. However, the basic comparative data on the languages of the world required for these analyses is often widely dispersed in hard to obtain sources. Here we outline how our Austronesian Basic Vocabulary Database (ABVD) helps remedy this situation by collating wordlists from over 500 languages into one web-accessible database. We describe the technology underlying the ABVD and discuss the benefits that an evolutionary bioinformatic approach can provide. These include facilitating computational comparative linguistic research, answering questions about human prehistory, enabling syntheses with genetic data, and safe-guarding fragile linguistic information.

A TABLE OF WORD FREQUENCIES DERIVED FROM 250,000 WORDS OF RECORDED INTERVIEWS WITH UNIVERSITY STUDENTS AND HOSPITAL PATIENTS IS PRESENTED. DATA FOR SUBSAMPLES OF 100,000 WORDS EACH FROM THE STUDENT PATIENT POPULATIONS ARE ALSO GIVEN TO PERMIT EVALUATION OF THEIR DIFFERENCES. A TOTAL OF 9699 DIFFERENT WORDS, OF WHICH 4097 OCCURRED ONLY ONCE IN THE COMPLETE SAMPLE, ARE LISTED.

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.

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.

A comprehensive count of bigram and trigram frequencies and versatilities was tabulated for words recorded by Ku{\v{c}}era and Francis. Totals of 577 different bigrams and 6,140 different trigrams were found. Their frequencies of occurrence and the number of different words in which they appeared are reported in this article.

A set of rating data was collected which compared the visual similarity of consonants to the auditory similarity of the same letters. Analysis of the rating patterns raises important questions about the use of similarity data in drawing conclusions about mode of memory coding in a variety of situations.

Written word frequency (e.g., Francis {\&} Kucera, 1982; Kucera {\&} Francis, 1967) constitutes a popular measure of word familiarity, which is highly predictive of word recognition. Far less often, researchers employ spoken frequency counts in their studies. This discrepancy can be attributed most readily to the conspicuous absence of a sizeable spoken frequency count for American English. The present article reports the construction of a 1.6-million-word spoken frequency database derived from the Michigan Corpus of Academic Spoken English (Simpson, Swales, {\&} Briggs, 2002). We generated spoken frequency counts for 34,922 words and extracted speaker attributes from the source material to generate relative frequencies of words spoken by each speaker category. We assess the predictive validity of these counts, and discuss some possible applications outside of word recognition studies.

Corpora are an important resource for both teaching and research. Arabic lacks sufficient resources in this field, so a research project has been designed to compile a corpus, which represents the state of the Arabic language at the present time and the needs of end-users. This report presents the result of a survey of the needs of teachers of Arabic as a foreign language (TAFL) and language engineers. The survey shows that a wide range of text types should be included in the corpus. Overall, our survey confirms our view that existing corpora are too narrowly limited in source-type and genre, and that there is a need for a freely-accessible corpus of contemporary Arabic covering a broad range of text-types. We have collected and published an initial version of the Corpus of Contemporary Arabic (CCA) to meet these design issues. The CCA is freely downloadable via WWW from http://www.comp.leeds.ac.uk/arabic.

CVCVC (319) words and paralogs previously assessed for associative reaction time (RT) by Taylor and Kimble were assessed for rated frequency (a′) and scaled rated meaningfulness (m′) following procedures used by Noble. Reliability of the a′ scale, based on three intergroup correlations, resulted in rs of .92, .90 and .89. Reliability of the m′ scale, based on an internal consistency test resulted in a mean discrepancy between the 128 empirical proportions and their corresponding theoretical proportions of 2.4; that is, the average error of reproducing all the original data from m′ scaled values was 2.4{\%}. The r between m′ and RT was .71.

Word associations to 40 homographic stimuli were scored in terms of the semantic features serving as S's apparent functional stimulus, and hierarchies related to the separate sets of features of homographs were determined. For most homographs the dominant meaning in the associative hierarchy was the more frequently occurring meaning in semantic counts, a finding in agreement with a spew-like principle of perceiving homographic stimuli.

Alfred Castaneda, Leila Snyder Fahel, Richard Odom, Associative Characteristics of Sixty-Three Adjectives and Their Relation to Verbal Paired-Associate Learning in Children, Child Development, Vol. 32, No. 2 (Jun., 1961), pp. 297-304

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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/.

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).

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.

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.

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.

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.

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 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.

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.

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.

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).

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.

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.

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.

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.