Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
This project was supervised by Prof. Esther Dromi, Tel-Aviv University. The aim of this project was to generate Hebrew lexical development norms for toddlers aged 12-24 months. The norms presented here can be used by scientists and language clinicians as reference to the expected developmental curves for research and practice purposes. Data were collected from Hebrew-speaking parents of 881 healthy monolingual toddlers (12-24 months old). Parents completed a web version of the Hebrew adaptation of the MacArthur-Bates Communicative Development Inventories - Words and Gestures (MB-CDI-WG) as well as a background questionnaire. Parents reported on their child’s receptive and expressive lexicons, and action and gesture use. Hebrew-speaking clinicians please refer to the general guidelines.
English Abstract: The article analyses the relationship between etymology of political neologisms, ways of their formation and methods of rendering them from English into Russian. Translation strategy is directed at the recipient of the translated text and should therefore be pragmatic and based on the functional and stylistic norms of the translation language. The author concludes that the most efficient way of word-formation in the English language is compounding (morphological neologisms) and derivation of new meaning for the already existing words (semantic neologisms). The translation methods demonstrating high potential are transliteration, calquing (loan translation) and descriptive translation. Adequate translation is based on the following criteria: conciseness, single interpretation, conformity with the linguistic norms of the translation language. Russian Abstract: В работе затрагиваются вопросы взаимосвязи между этимологией, способами образования политических неологизмов и приемами их передачи с английского языка на русский. Выбор переводческой стратегии ориентирован на получателя текста перевода, поэтому должен обеспечивать прагматические задачи в соответствии с функционально-стилистическими нормами языка перевода. В работе делается вывод о том, что наиболее продуктивным способом образования новых слов является процесс словосложения (морфологические неологизмы) и наделения уже существующих в языке слов новым значением (семантические неологизмы); самыми эффективными приемами перевода неологизмов являются транслитерация, калькирование и описательный перевод. Критериями адекватного перевода политических неологизмов служат краткость, однозначность толкования, соответствие нормам языка перевода.
It is common knowledge and prescribed in all normative Portuguese grammars that the verb must agree in number and person with its subject, whether the latter is superposed or postponed to the verb. The lack of agreement between subject and verb (concordância verbal varíavel) is seen by users with a scholastic education as being wrong and linked to the poorest social strata, that is, with a low or zero level of education. However, cases of lack of agreement are not uncommon in informal speech of users with medium or high levels of education. This dichotomy between linguistic norms and orality, and its perception by the Brazilian population (i.e. lack of agreement as a sign of a lower educational and social level) can be verified also in the artistic reproduction, namely in the filmic dialogues of Brazilian national cinema, where verbal agreement variation is used to typify characters with little or no education. This paper will attempt to analyse how this linguistic phenomenon is interpreted by film discourse, i.e. a reproduction of orality. First, the linguistic issue will be presented, that is, the agreement and the lack of it in some registers of Brazilian Portuguese, and a brief presentation of the type of data on which this research was conducted (filmic dialogues from Brazilian films). Then, cases of variable verbal agreement (hereafter CVV) of the first plural person (hence 1PP) will be presented. In this perspective, the analysis of the alternation of use between two pronominal forms in subject function for the 1PP will be considered as a possible cause for the variation of verbal agreement between verb and subject with the 1PP. The approach adopted in this research brings together the variational studies and tools of Corpora Linguistics in an attempt to offer a critical view of the linguistic choices involved in film production.
comptes-rendus sur les ressources numriques 171 presents, LEME also happens to be one of the most robust bibliographies of early modern language-learning documents available, covering 250 works and providing a bibliography totalling 1,400 works under the remit of "lexicons" or "language-learning resources."A scholar with no interest beyond the bibliographic data for all these language-learning documents will still find LEME to be a rich resource as a starting point.As a companion to other linguistic databases such as the OED and its Historical Thesaurus, LEME offers a way to triangulate contextualization within editorial projects, provides additional details regarding pronunciation for metrical and other voice-based scholarship and practice, and opened the landscape for subsequent projects like VARD. 5 In our post-EEBO-TCP data deluge, LEME's focus on material linguistic history is utterly essential for scholars and practitioners of early modern language, variation, and change.
Recurrent neural networks (RNNs) have recently achieved remarkable successes in a number of applications. However, the huge sizes and computational burden of these models make it difficult for their deployment on edge devices. A practically effective approach is to reduce the overall storage and computation costs of RNNs by network pruning techniques. Despite their successful applications, those pruning methods based on Lasso either produce irregular sparse patterns in weight matrices, which is not helpful in practical speedup. To address these issues, we propose structured pruning method through neuron selection which can reduce the sizes of basic structures of RNNs. More specifically, we introduce two sets of binary random variables, which can be interpreted as gates or switches to the input neurons and the hidden neurons, respectively. We demonstrate that the corresponding optimization problem can be addressed by minimizing the L0 norm of the weight matrix. Finally, experimental results on language modeling and machine reading comprehension tasks have indicated the advantages of the proposed method in comparison with state-of-the-art pruning competitors. In particular, nearly 20 x practical speedup during inference was achieved without losing performance for language model on the Penn TreeBank dataset, indicating the promising performance of the proposed method
The article discusses the current changing of linguistic norms in English as a lingua franca of global communication nowadays. It aims at both determining the causes of language deviations and analyzing language errors as well as their impact on the effectiveness of the English language communication. Based on the analysis of abundant empirical material, we prove that language innovations are caused by the immanent structural, functional, and pragmatic variability / instability of the English language; they are also associated with cognitive and sociocultural evolution. The research methodology includes: a corpus-based analysis of speech errors; interpretative, context and discourse analyses of the sources of language errors, as well as their distribution, adaptation, habitualization, legitimization, and regulation. We discuss the degree of influence of these processes on native and non-native speakers. Special attention is paid to multilingual interference and the Internet language creation. The findings show that it is impossible to separate language errors from language innovations today. Such conventional governing principles of error normalization as credibility, codification, and approval are still playing an important role while the demographic and geographic principles are losing their significance. The Internet communication often proliferates error normalization processes, which result in evaluating (accepting or rejecting) any innovation according to the principle of “virtual validity”. In conclusion, the English language status as a language of the international communication significantly transforms its norms, rules, and traditions. We think, this will not worsen it, but allow people of different nationalities to communicate in English more effective using their “English variant”, which is the most adapted one to their cognitive, functional and pragmatic needs.
Cet article presente la creation d’un treebank journalistique serbe, ParCoJour. Il est compose de 30K tokens et dote de trois couches d’annotation: etiquetage morphosyntaxique, lemmatisation et annotation syntaxique. Une fois construit, ParCoJour a ete utilise dans trois experiences afin d’evaluer l’impact du domaine textuel sur le parsing du serbe en comparant les performances de Talismane, un systeme par apprentissage automatique, sur deux types de corpus, journalistique et litteraire: 1) parsing du corpus journalistique avec un modele entraine sur le corpus journalistique; 2) parsing du corpus journalistique avec un modele entraine sur le corpus litteraire; 3) parsing du corpus litteraire avec un modele entraine sur le corpus journalistique. Les resultats sont compares a ceux ou les deux corpus relevaient du domaine litteraire. Le changement de domaine textuel dans la deuxieme et la troisieme experience entraine une baisse des performances, mais les resultats de parsing restent satisfaisants.
Like odor identification, remote odor memory, reflected in familiarity ratings, is impaired in AD (Murphy, Nature Reviews Neurology, 2019). We investigated the relative abilities of standard screening (MMSE), odor identification and remote odor memory to predict transitions from amnestic MCI (aMCI) to AD in a sample from the UCSD ADRC. The sample contained 170 controls, 210 AD, 26 aMCI converters to AD and 42 aMCI non-converters. A receiver operating characteristic (ROC) curve plots the trade-off between sensitivity and specificity. The area under the curve (AUC) indicates how well a marker discriminates patients from controls. Analyses showed higher predictive value for converting from aMCI to AD in ApoE ε4+ carriers for odor familiarity, odor identification and for the combination than for the MMSE. ROC/AUCs for the conversion from aMCI to AD have ranged from.63 -.67 for CSF biomarkers. Odor familiarity and odor identification had similar AUC values; however, combining odor familiarity and odor identification produced an ROC/AUC value of 1.0 in ε4 carriers, appreciably higher than for MMSE alone (.58). Olfactory biomarkers show real promise as early, non-invasive indicators of disease, particularly in samples enriched with ε4 carriers. Although odor identification has been the focus of olfactory biomarker work, the results suggest that other measures of olfactory function have the potential to enhance prediction. Combining odor familiarity and odor identification produced a predictive value of 1.0 in ε4 carriers. The results warrant further investigation into the potential for enhancing drug trials and clinical screening. Supported by NIH grants R01AG004085-26 (CM) and P50AG005131 (UCSD ADRC). We thank the UCSD ADRC and particularly Drs. Douglas Galasko and David Salmon.
Cultural norms for the experience, expression, and regulation of emotion vary widely between individualistic and collectivistic cultures. Collectivistic cultures value conformity, social harmony, and social status hierarchies, which demand sensitivity and focus to broader social contexts, such that attention is directed to contextual emotion information to effectively function within constrained social roles and suppress incongruent personal emotions. By contrast, individualistic cultures valuing autonomy and personal aspirations are more likely to attend to central emotion information and to reappraise emotions to avoid negative emotional experience. Here we examined how culture affects perceptual strategies employed during emotion regulation, particularly during cognitive reappraisal and emotional suppression. Eye movements were measured while healthy young adult participants viewed negative International Affective Picture System (IAPS) images and regulated emotions by using either strategies of reappraisal (19 Asian American, 21 Caucasian American) or suppression (21 Asian American, 23 Caucasian American). After image viewing, participants rated how negative they felt as a measure of subjective emotional experience. Consistent with prior studies, reappraisers made lower negative valence ratings after regulating emotions than suppressers across both Asian American and Caucasian American groups. Although no cultural variation was observed in subjective emotional experience during emotion regulation, we found evidence of cultural variation in perceptual strategies used during emotion regulation. During middle and late time periods of emotional suppression, Asian American participants made significantly fewer fixations to emotionally salient areas than Caucasian American participants. These results indicate cultural variation in perceptual differences underlying emotional suppression, but not cognitive reappraisal.
This paper is centered around two main contributions: the first one consists in introducing several procedures for generating random dependency trees with constraints; we later use these artificial trees to compare their properties with the properties of natural trees (i.e trees extracted from treebanks) and analyze the relationships between these properties in natural and artificial settings in order to find out which relationships are formally constrained and which are linguistically motivated. We take into consideration five metrics: tree length, height, maximum arity, mean dependency distance and mean flux weight, and also look into the distribution of local configurations of nodes. This analysis is based on UD treebanks (version 2.3, Nivre et al. 2018) for four languages: Chinese, English, French and Japanese.
The smart home brings together devices, the cloud, data, and people to make home living more comfortable and safer. Trigger-action programming enables users to connect smart devices using if-this-then-that (IFTTT)-style rules. With the increasing number of devices in smart home systems, multiple running rules that act on actuators in contradictory ways may cause unexpected and unpredictable interference problems, which can put residents and their belongings at risk. Previous studies have considered explicit interference problems related to multiple rules targeting a single actuator, whereas implicit interference (interference across different actuators) detection is still challenging and not yet well studied owing to the effort-intensive and time-consuming annotation work of obtaining device information. The lack of knowledge about devices is a critical reason that affects the accuracy and efficiency in implicit interference detection. In this article, we propose A3ID, an automatic detection method for implicit interference based on knowledge graphs. Using natural language processing (NLP) techniques and a lexical database, A3ID can extract knowledge of devices from a knowledge graph, including functionality, effect, and scope. Then, it analyzes and detects interferences among the different devices semantically in three steps, without human intervention. Furthermore, it provides user-friendly explanations in a well-designed structure to specify possible reasons for the implicit interference problems. Our experiment on 11 859 IFTTT-style rules shows that A3ID outperforms state-of-the-art methods by more than 33% in the F1-score for the detection of implicit interference. Moreover, evaluations on an extended data set for devices from ConceptNet (a knowledge graph) and five smart home systems suggest that A3ID also has favorable performance with other devices not limited to the smart home domain.
Stack-augmented recurrent neural networks (RNNs) have been of interest to the deep learning community for some time. However, the difficulty of training memory models remains a problem obstructing the widespread use of such models. In this paper, we propose the Ordered Memory architecture. Inspired by Ordered Neurons (Shen et al., 2018), we introduce a new attention-based mechanism and use its cumulative probability to control the writing and erasing operation of the memory. We also introduce a new Gated Recursive Cell to compose lower-level representations into higher-level representation. We demonstrate that our model achieves strong performance on the logical inference task (Bowman et al., 2015) and the ListOps (Nangia and Bowman, 2018) task. We can also interpret the model to retrieve the induced tree structure, and find that these induced structures align with the ground truth. Finally, we evaluate our model on the Stanford Sentiment Treebank tasks (Socher et al., 2013), and find that it performs comparatively with the state-of-the-art methods in the literature.
Identifying eye-movement measures as objective indicators of mind wandering seems to be a work in progress. We reviewed research comparing eye movements during self-categorized episodes of normal versus mindless reading and found little consensus regarding the specific measures that are sensitive to attentional decoupling during mind wandering. To address this issue of inconsistency, we conducted a new, high-powered eye-tracking experiment and considered all previously identified mind-wandering indicators. In our experiment, only three measures (reading time, fixation count, and first-fixation duration) positively predicted self-categorized mindless reading. Aside from these single measures, the word-frequency effect was found to be generally less pronounced during mindless-reading than during normal-reading episodes. To additionally test for convergent validity between the objective and subjective mind-wandering measures, we utilized eye-movement measures as well as thought reports, to examine the effect of metacognitive awareness on mind-wandering behavior. We expected that participants anticipating a difficult comprehension test would mind wander less during reading than would those anticipating an easy test. Although we were able to induce metacognitive expectancies about task difficulty, we found no evidence that these difficulty expectancies affected either subjectively reported or objectively measured mind wandering.
With the rapid development of the internet, social media has become an essential tool for getting information, and attracted a large number of people join the social media platforms because of its low cost, accessibility and amazing content. It greatly enriches our life. However, its rapid development and widespread also have provided an excellent convenience for the range of fake news, people are constantly exposed to fake news and suffer from it all the time. Fake news usually uses hyperbole to catch people’s eyes with dishonest intention. More importantly, it often misleads the reader and causes people to have wrong perceptions of society. It has the potential for negative impacts on society and individuals. Therefore, it is significative research on detecting fake news. In the paper, we built a model named SMHA-CNN (Self Multi-Head Attention-based Convolutional Neural Networks) that can judge the authenticity of news with high accuracy based only on content by using convolutional )
SUD is an annotation scheme for syntactic dependency treebanks, near isomorphic to UD (Universal Dependencies). Contrary to UD, it is based on syntactic criteria (favoring functional heads) and the relations are defined on distributional and functional bases. In this paper, we will recall and specify the general principles underlying SUD, present the updated set of SUD relations, discuss the central question of MWEs, and introduce an orthogonal layer of deep-syntactic features converted from the deep-syntactic part of the UD scheme.
The article is dedicated to a lexical analysis of adjectival nouns with the –ota suffix in Adam Mickiewicz’s Pan Tadeusz. Out of five nouns, viewed against the 19th century language norm, four are definitely archaisms. These are the words: ciemnota meaning <ciemność - darkness>, szczodrota (generosity) and some uses of the words cnota (virtue) and hołota (the poor and badly educated). On top of the noun ciemnota meaning <ciemność - darkness> which is a typical Easter Borderlands archaism, they should be traced back to the literary tradition. This is corroborated by the fact that the poet used them in an informed way to make the text sound like Old Polish. Therefore, with respect to the function and the archaic origin, the nouns with the –ota suffix in the text of “Pan Tadeusz” can be divided into two groups: stylistic archaisms which come from the literary tradition (szczodrota, partly cnota and hołota) plus systemic archaisms which belong to Mickiewicz’s idiolectal system (ciemnota meaning <ciemność - darkness>).
Emotion science relies upon both facial and scene stimuli that evoke targeted emotions with regard to hedonic valence (i.e., pleasant or unpleasant) and emotional arousal or intensity. While both stimulus types have well established normative ratings via pleasantness and activation/arousal ratings, there are few direct comparisons of these stimuli. Using an auditory probe reaction time paradigm (probe RT), emotional scenes (i.e., the IAPS) were found to be more emotionally engaging compared to facial stimuli (i.e., KDEF). However, some facial sets are more engaging than others--namely the NimStim evokes significantly greater emotional arousal than the KDEF. Here, we plan to compare facial stimuli and scenes from the IAPS using the probe RT paradigm. We plan to compare the two stimulus types (faces vs. scenes) and compare the two facial stimulus sets (KDEF vs. NimStim) with the scenes from the IAPS. We predict that the NimStim will be significantly more emotionally engaging than the KDEF stimuli and that the emotional scenes will be significantly more engaging than both facial stimulus sets. While emotional scenes may be more emotionally engaging than emotional facial stimuli, the current study will extend the literature by demonstrating that certain facial stimuli (e.g., the NimStim) are more emotionally engaging than others (i.e., the KDEF). These findings, for instance, may inform social anxiety research, which relies heavily on facial stimulus sets, by providing a reaction time index of emotional engagement that compliments self-report emotional judgments to guide the emotion researcher in stimulus selection.
It analyzes the literary and journalistic work of ngel de Campo (1868-1908) and the various phonetic, morphosyntactic and lexical records that appear in it both in dialogues and in the different narrative voices in order to see if its vitality continues in the Mexican dialect current or has followed other courses. The analyzed phenomena express the popular and cultured linguistic norms of the Spanish that was spoken in Mexico City at the end of the XIX century and the beginning of the XX, thanks to the record that the author makes especially of the marginalized social classes of the Mexican capital.
Neural parsers obtain state-of-the-art results on benchmark treebanks for constituency parsing -- but to what degree do they generalize to other domains? We present three results about the generalization of neural parsers in a zero-shot setting: training on trees from one corpus and evaluating on out-of-domain corpora. First, neural and non-neural parsers generalize comparably to new domains. Second, incorporating pre-trained encoder representations into neural parsers substantially improves their performance across all domains, but does not give a larger relative improvement for out-of-domain treebanks. Finally, despite the rich input representations they learn, neural parsers still benefit from structured output prediction of output trees, yielding higher exact match accuracy and stronger generalization both to larger text spans and to out-of-domain corpora. We analyze generalization on English and Chinese corpora, and in the process obtain state-of-the-art parsing results for the Brown, Genia, and English Web treebanks.
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project.
Relation classification is a vital task in natural language processing, and it is screening for semantic relation between clauses in texts. This paper describes a study of relation classification on Chinese compound sentences without connectives. There exists an implicit relation in a compound sentence without connectives, which makes it difficult to realize the recognition of relation. The major challenges that relation classification modeling faces are how to obtain the contextual representation of sentence and relation dependence features between clauses. To solve this problem, we propose a novel Inatt-MCNN model to extract sentence features and classify relations by combining multi-channel CNN and Inner-attention mechanism. This network structure utilizes CNN to extract local features of sentences and Inner-attention to capture sentence-level feature representations for this relation classification task. Besides, since the Inner-attention is based on Bi-LSTM, the global and long-term dependence semantic information can be well obtained in Inatt-MCNN to promote the model performance. We conduct experiments on two public Chinese discourse datasets: the Chinese compound sentence corpus (CCCS) dataset and the Tsinghua Chinese Treebank(TCT) dataset. Compared with the previous public methods, Inatt-MCNN model has superior performance and achieves the highest accuracy, especially on the CCCS dataset.
For sequence models with large vocabularies, a majority of network parameters lie in the input and output layers. In this work, we describe a new method, DeFINE, for learning deep token representations efficiently. Our architecture uses a hierarchical structure with novel skip-connections which allows for the use of low dimensional input and output layers, reducing total parameters and training time while delivering similar or better performance versus existing methods. DeFINE can be incorporated easily in new or existing sequence models. Compared to state-of-the-art methods including adaptive input representations, this technique results in a 6% to 20% drop in perplexity. On WikiText-103, DeFINE reduces the total parameters of Transformer-XL by half with minimal impact on performance. On the Penn Treebank, DeFINE improves AWD-LSTM by 4 points with a 17% reduction in parameters, achieving comparable performance to state-of-the-art methods with fewer parameters. For machine translation, DeFINE improves the efficiency of the Transformer model by about 1.4 times while delivering similar performance.
Purpose This paper aims to describe the structure of an aligned Serbian-German literary corpus (SrpNemKor) contained in a digital library Bibliša. The goal of the research was to create a benchmark Serbian-German annotated corpus searchable with various query expansions. Design/methodology/approach The presented research is particularly focused on the enhancement of bilingual search queries in a full-text search of aligned SrpNemKor collection. The enhancement is based on using existing lexical resources such as Serbian morphological electronic dictionaries and the bilingual lexical database Termi. Findings For the purpose of this research, the lexical database Termi is enriched with a bilingual list of German-Serbian translated pairs of lexical units. The list of correct translation pairs was extracted from SrpNemKor, evaluated and integrated into Termi. Also, Serbian morphological e-dictionaries are updated with new entries extracted from the Serbian part of the corpus. Originality/value A bilingual search of SrpNemKor in Bibliša is available within the user-friendly platform. The enriched database Termi enables semantic enhancement and refinement of user’s search query based on synonyms both in Serbian and German at a very high level. Serbian morphological e-dictionaries facilitate the morphological expansion of search queries in Serbian, thereby enabling the analysis of concepts and concept structures by identifying terms assigned to the concept, and by establishing relations between terms in Serbian and German which makes Bibliša a valuable Web tool that can support research and analysis of SrpNemKor.
Perception of emotions and adequate responses are key factors of a successful conversational agent. However, determining emotions in a healthcare setting depends on multiple factors such as context and medical condition. Given the increase of interest in conversational agents integrated in mobile health applications, our objective in this work is to introduce a concept for analyzing emotions and sentiments expressed by a person in a mobile health application with a conversational user interface. The approach bases upon bot technology (Synthetic intelligence markup language) and deep learning for emotion analysis. More specifically, expressions referring to sentiments or emotions are classified along seven categories and three stages of strengths using treebank annotation and recursive neural networks. The classification result is used by the chatbot for selecting an appropriate response. In this way, the concerns of a user can be better addressed. We describe three use cases where the approach could be integrated to make the chatbot emotion-sensitive.
The interest on French in sub-Saharan Africa is certainly related to the fact that it differs in many ways from French in France. The appropriation of French as a second language by African speakers has fostered the birth of endogenous norms with peculiarities that affect phonetic-phonological, lexical, morphological, syntactic and, also pragmatic-textual levels. However, research on the latter aspect remains at an embryonic stage. Concerning Côte d'Ivoire and its complex linguistic landscape consisting of some sixty languages, it often occurs that French serves both as vehicular and vernacular. While its phonetic, lexical and morpho-syntactic features have already been extensively researched, this is not the case for the pragmatic and textual aspects. This work intends to fill in the gap by focusing on the uses of the borrowed discourse marker dɛ in Ivorian popular French.
The aim of this article is to analyse the attitude towards the linguistic norm of the first-year students of Romance Philology in Warsaw through the auto-narration. By this tool, belonging to the qualitative methodology, the learner performs a retrospective introspection and thus ‘evaluates’ the learning, in our context – his learning of grammar. The stories of students-future philologists will be analysed according to the following aspects: the importance given to the linguistic norm (understood here as the need for grammatical correction), the perception of its ‘utility’ in relation to other competences and language subsystems, the relationship between awareness of the norm and the effectiveness / quality of communication in a foreign language.
Co-occurrence models have been of considerable interest to psychologists because they are built on very simple functionality. This is particularly clear in the case of prediction models, such as the continuous skip-gram model introduced in Mikolov, Chen, Corrado, and Dean (2013), because these models depend on functionality closely related to the simple Rescorla–Wagner model of discriminant learning in nonhuman animals (Rescorla & Wagner, 1972), which has a rich history within psychology as a model of many animal learning processes. We replicate and extend earlier work showing that it is possible to extract accurate information about syntactic category and morphological family membership directly from patterns of word co-occurrence, and provide evidence from four experiments showing that this information predicts human reaction times and accuracy for class membership decisions.
espanolEn el presente trabajo se delinea la aportacion de la lengua vasca a la formacion de la norma castellana durante la Edad Media y el Siglo de Oro. Para llegar a tal fin, se perfila primero el marco historico de redes sociales y linguisticas operantes en el contacto del castellano con el euskera desde los origenes de la convivencia vasco-latino-romanica hasta el siglo XVII y se tiene en cuenta, despues, la doble direccion en el contacto vasco-castellano-romanico, sobrevenido historicamente tanto en la direccion del euskera hacia el castellano como del castellano al vascuence, sin olvidar que ha afectado tambien a territorio hoy frances. EnglishThe present article studies the Basque Language contribution to the Castilian linguistic Norm in the Middle Ages and the Golden Age. For this purpose will be first designed the historical frames operating since ancient times on the linguistic contact between Basque and Romance Languages in order to explain how the direction of the Basque-Romance contact has occurred in both directions respectively, taken into account that France domain has been also concerned.
In this paper, we propose an Arabic word segmentation technique based on a bi-directional long short-term memory deep neural network. This paper addresses the two tasks of word segmentation only and word segmentation for nine cases of the rewrite. Word segmentation with a rewrite concerns inferring letters that are dropped or changed when the main word unit is attached to another unit, and it writes these letters back when the two units are separated as a result of segmentation. We only use binary labels as indicators of segmentation positions. Therefore, label 1 is an indicator of the start of a new word (split) in a sequence of symbols not including whitespace, and label 0 is an indicator for any other case (no-split). This is different from the mainstream feature representation for word segmentation in which multi-valued labeling is used to mark the sequence symbols: beginning, inside, and outside. We used the Arabic Treebank data and its clitics segmentation scheme in our experiments. The trained model without the help of any additional language resources, such as dictionaries, morphological analyzers, or rules, achieved a high F1 value for the Arabic word segmentation only (98.03%) and Arabic word segmentation with the rewrite (more than 99% for frequent rewrite cases). We also compared our model with four state-of-the-art Arabic word segmenters. It performed better than the other segmenters on a modern standard Arabic text, and it was the best among the segmenters that do not use any additional language resources in another test using classical Arabic text.
We study how to leverage off-the-shelf visual and linguistic data to cope with out-of-vocabulary answers in visual question answering task. Existing large-scale visual datasets with annotations such as image class labels, bounding boxes and region descriptions are good sources for learning rich and diverse visual concepts. However, it is not straightforward how the visual concepts can be captured and transferred to visual question answering models due to missing link between question dependent answering models and visual data without question. We tackle this problem in two steps: 1) learning a task conditional visual classifier, which is capable of solving diverse question-specific visual recognition tasks, based on unsupervised task discovery and 2) transferring the task conditional visual classifier to visual question answering models. Specifically, we employ linguistic knowledge sources such as structured lexical database (e.g. WordNet) and visual descriptions for unsupervised task discovery, and transfer a learned task conditional visual classifier as an answering unit in a visual question answering model. We empirically show that the proposed algorithm generalizes to out-of-vocabulary answers successfully using the knowledge transferred from the visual dataset.
There are few studies of user interaction with music libraries comprising solely of unfamiliar music, despite such music being represented in national music information centre collections. We aim to develop a system that encourages exploration of such a library. This study investigates the influence of 69 users’ pre-existing musical genre and feature preferences on their ongoing continuous real-time psychological affect responses during listening and the acoustic features of the music on their liking and familiarity ratings for unfamiliar art music (the collection of the Australian Music Centre) during a sequential hybrid recommender-guided interaction. We successfully mitigated the unfavorable starting conditions (no prior item ratings or participants’ item choices) by using each participant’s pre-listening music preferences, translated into acoustic features and linked to item view count from the Australian Music Centre database, to choose their seed item. We found that first item liking/familiarity ratings were on average higher than the subsequent 15 items and comparable with the maximal values at the end of listeners’ sequential responses, showing acoustic features to be useful predictors of responses. We required users to give a continuous response indication of their perception of the affect expressed as they listened to 30-second excerpts of music, with our system successfully providing either a “similar” or “dissimilar” next item, according to—and confirming—the utility of the items’ acoustic features, but chosen from the affective responses of the preceding item. We also developed predictive statistical time series analysis models of liking and familiarity, using music preferences and preceding ratings. Our analyses suggest our users were at the starting low end of the commonly observed inverted-U relationship between exposure and both liking and perceived familiarity, which were closely related. Overall, our hybrid recommender worked well under extreme conditions, with 53 unique items from 100 chosen as “seed” items, suggesting future enhancement of our approach can productively encourage exploration of libraries of unfamiliar music.
Issues surrounding English for Academic Purposes and its use by non-native English speakers in higher education have become increasingly significant in recent years, fueled both by increased international student mobility and increased linguistic and cultural diversity within and outside of the student body. As well as posing language-related challenges, the transfer of non-native English speakers to an English speaking foreign university also demands the negotiation of new university expectations, channeled through a new cultural environment. While academic literacies research has identified that concepts such as power, identity, and culture play a role in academic writing, the navigation of these aspects in academic writing have not yet been studied thoroughly. Consequently, this study analyzes the ways in which non-native English speaking (NNES) students articulate their navigation of power, identity, and culture within their own academic writing at a tertiary institution in Ireland. Data informing this study was gathered through questionnaires and followed by in-depth case studies of students interview responses analyzed through discourse analysis. The findings suggest that while participants generally positively reflect on their ability to negotiate academic writing through the English language, there is nonetheless a high level of conflict between dominant linguistic norms and the students’ expression of their identity and culture. These findings suggest a need to increase focus on academic literacies in tertiary institutions in order to aid the negotiation of these aspects and to increase the academic success of non-native English speakers.
Encoding and retrieval of emotionally arousing stimuli depend on the activation of multiple interconnected brain regions, with people showing differences in their individual strength of emotional perception and recollection. Understanding the association between these brain regions and the behavioral outcome might therefore have important clinical implications as dysfunctional emotional memory processes are characteristic of many psychiatric disorders. Based on behavioral and fMRI data collected from healthy young adults (N = 1'385), we investigated brain activation patterns, arousal ratings and memory performance during encoding and retrieval of negative and neutral pictures. We performed multi-voxel pattern analysis (MVPA) and voxel-wise association analyses. Subjects' individual strength of perceived arousal at encoding and subjects' memory performance at recognition could be predicted from the fMRI data of the respective tasks by using a topographically identical network of brain regions. This network was mainly left lateralized including dense clusters of voxels in the occipital and parietal lobe and including the amygdala. Voxel-wise association analyses confirmed the close link between the brain activation of both tasks and their relation to the respective behavioral outcome. These results point to the importance of the here identified brain network for emotional memory processes in health and, possibly, disease.
Abstract Generating novel design concepts is a cornerstone for producing innovative products. Although many methods have been proposed for supporting the task, their performance depends on human ability. The goal of this research is to build a method supporting designers to generate novel design concepts with the knowledge of what factors have positive effects on the novelty. Toward the goal, this research assumes that the more distant two function concepts chosen, the more novel idea would come up with by the combination of the two concepts. Based on the assumption, this paper introduces a notion of novelty potential of the combination of two function concepts, and proposes a method to assess it by the function similarity. It is calculated with the integration of a lexical database for natural language called WordNet and a distributional semantics method called word2vec. The proposed method is adapted to case studies in which students perform design concept generation for given design tasks. The correlation analysis is performed to verify the assessment performance of the proposed method. This paper discusses its possibility based on the results of the case studies.
Morphological segmentation has traditionally been modeled with non-hierarchical models, which yield flat segmentations as output. In many cases, however, proper morphological analysis requires hierarchical structure -- especially in the case of derivational morphology. In this work, we introduce a discriminative, joint model of morphological segmentation along with the orthographic changes that occur during word formation. To the best of our knowledge, this is the first attempt to approach discriminative segmentation with a context-free model. Additionally, we release an annotated treebank of 7454 English words with constituency parses, encouraging future research in this area.
In this paper, we present a Linguistic Informed Multi-Task BERT (LIMIT-BERT) for learning language representations across multiple linguistic tasks by Multi-Task Learning (MTL). LIMIT-BERT includes five key linguistic syntax and semantics tasks: Part-Of-Speech (POS) tags, constituent and dependency syntactic parsing, span and dependency semantic role labeling (SRL). Besides, LIMIT-BERT adopts linguistics mask strategy: Syntactic and Semantic Phrase Masking which mask all of the tokens corresponding to a syntactic/semantic phrase. Different from recent Multi-Task Deep Neural Networks (MT-DNN) (Liu et al., 2019), our LIMIT-BERT is linguistically motivated and learning in a semi-supervised method which provides large amounts of linguistic-task data as same as BERT learning corpus. As a result, LIMIT-BERT not only improves linguistic tasks performance but also benefits from a regularization effect and linguistic information that leads to more general representations to help adapt to new tasks and domains. LIMIT-BERT obtains new state-of-the-art or competitive results on both span and dependency semantic parsing on Propbank benchmarks and both dependency and constituent syntactic parsing on Penn Treebank.
Borderline personality disorder (BPD) is a diagnosis characterized by intense and labile emotion; dialectical behavior therapy, a common treatment for BPD, aims to reduce the intensity and lability of clients' emotion through multiple methods, some of which occur in the therapy session, with the expectation that changes will generalize to the rest of clients' lives. However, little research has examined how BPD clients' affect presents and varies in session or whether affect in session reflects patients' patterns of affect outside of treatment. This study had 2 aims: (a) to explore changes in clients' positive and negative affect in therapy, and (b) to assess if the severity of client psychopathology relates to affect in treatment. Positive and negative affect ratings were collected from clients (N = 73) at the start and end of every individual therapy session (total sessions = 1,474). Hierarchical linear modeling and linear regression were used to examine patterns of affect and assess the relationship between affect and severity. Results indicated that positive affect increased while negative affect decreased between the start and end of sessions, with the same pattern of change in presession affect from week to week. In addition, increased BPD severity was associated with lower presession positive affect ratings and higher negative affect ratings. Further exploration is needed to assess which dialectical behavior therapy treatment processes contribute to changes in in-session affect and how in-session affect relates to treatment outcomes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to train models for jointly predicting fine-grained temporal relations and event durations. We report strong results on our data and show the efficacy of a transfer-learning approach for predicting categorical relations.
In three experiments we investigated whether memory-independent evaluative conditioning (EC) and other memory-independent contingency learning (CL) effects occur in the valence contingency task (VCT). In the VCT, participants respond to the valence of a target word that is preceded by a nonword. Across trials, each nonword is mostly combined with either positive or negative targets. Schmidt and De Houwer (2012. Contingency learning with evaluative stimuli. Experimental Psychology, 59, 175–182. doi:10.1027/1618-3169/a000141) showed faster and more often correct responses on trials that conformed to this contingency. Additionally, the authors found EC on valence ratings assessed after the VCT. All effects occurred also in the absence of contingency memory. Our Experiments 1a and 1b replicated the CL effects on measures assessed during the VCT (RT, errors) and showed that they occurred in the absence of contingency memory, but they did not replicate the EC effect assessed after the VCT. In Experiment 2, we tested whether this dissociation between EC and other CL effects was due to the different phases (during vs. after VCT) with a CL measure that could be used in both phases. On this measure, the CL effect was memory-dependent after, but not during the VCT. Across measures and experiments, we thus find memory-independent CL during the VCT, but not afterwards.
Although there is a wide consensus on how sleep processes declarative memories, how sleep affects emotional memories remains elusive. Moreover, studies assessing the long-term effect of sleep on emotional memory consolidation are scarce. Studies testing subclinical populations characterized by REM abnormalities are also lacking. Here we aimed to (i) investigate the fate of emotional memories and the potential unbinding (or preservation) between content and affective tone over time (i.e., 1 week), (ii) explore the role of seven nights of sleep (recorded via actigraphy) in emotional memory consolidation, and (iii) assess whether participants with self-reported mild-moderate depressive symptoms forget less emotional information compared to participants with low depression symptoms. We found that, although at the immediate recognition session emotional information was forgotten more than neutral information, a week later it was forgotten less than neutral information. This effect was observed both in participants with low and mild-moderate depressive symptoms. We also observed an increase in valence rating over time for negative pictures, whereas perceived arousal diminished a week later for both types of stimuli (unpleasant and neutral); an initial decrease was already observable at the immediate recognition session. Interestingly, we observed a negative association between sleep efficiency across the week and change in memory discrimination for unpleasant pictures over time, i.e., participants who slept worse were the ones who forgot less emotional information. Our results suggest that emotional memories are resistant to forgetting, particularly when sleep is disrupted, and they are not affected by non-clinical depression symptomatology.
In the light of the most recent critical debate, sixteenth-century Petrarchism has been divested of the simple dichotomy between norm and rejection, similarity and dissimilarity, imitation and deviation in relation to Petrarch’s model or Bembo’s codification, and qualified as a complex and composite movement in which both significant constants and equally significant variations should be identified. In the frame of this dialectic, we analyse Michelangelo Buonarroti’s Rime in comparison to the original model of Rerum vulgarium fragmenta. The analysis highlights the fact that Michelangelo’s genius distorts the reference model and deviates from it in a material, tragic and expressionist sense, rather than offering a harmonious result of a strict observance of Petrarchism. Michelangelo, however, achieves this effect by employing the same rhetorical and expressive tools as Petrarch. The paper presents a comparative analysis illustrated by numerous examples of the rhetorical figures of antithesis, oxymoron, synonymy and a wide array of metaphors and lexical choices.