Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
The contrast between the contextual and general meaning of a word serves as an important clue for detecting its metaphoricity. In this paper, we present a deep neural architecture for metaphor detection which exploits this contrast. Additionally, we also use cost-sensitive learning by re-weighting examples, and baseline features like concreteness ratings, POS and WordNet-based features. The best performing system of ours achieves an overall F1 score of 0.570 on All POS category and 0.605 on the Verbs category at the Metaphor Shared Task 2018.
The article is devoted to the problem of identifying the stylistic functions of addresses, which are used in Internet communication. To achieve this goal, the author has solved several problems. For the first, the main features of Internet communication are anonymity, mediation, distance and frequent violation of linguistic norms. The last attribute refers to the adresses, which are used in Internet messages. For the second, official and household addreses are used in the Internet communication,. The stylistic function of the official addresses is the indication of the status and social role of the addressee. The stylistic function of household appeals is the indication of proximity between communication participants, the emphasis on positive or negative connotations of addresses.In addition, household addresses reflect the new social trends associated with the using of e-mail, aliases etc. The main conclusions of this research: the greatest number of the addresses in Internet communication is recorded in the materials of business correspondence. The users of the network very rarely use the household addresses. The author believes that the main reason for such quantitative dynamics is the avoidance or inability of addressees to show an emotional attitude to their interlocutor.
بنك المشجّرات محلّل حاسوبيّ للظّواهر التّركيبيّة في اللّغة العربيّة، استثمر مبادئ نظريّة التّحكّم والرّبط التّوليديّة، وحوسباتها وتصوّراتها للنّحو الكلّيّ، غايته في ذلك بناء نظام حوسبيّ آليّ، يحاكي في اشتغاله النّظام الحوسبيّ اللّغويّ الطّبيعيّ. وقد حقّق بنك المشجّرات نتائج مهمّة في هذا الشّأن، تتمثّل في بلوغه الانتظام والتّناسق في معالجة الأبنية الإعرابيّة، لكنّ العمل لم يخل من هنات، أهمّها عدم اتّسام السّيرورة الاشتقاقيّة بالخاصّيّة التّكراريّة المميّزة للّغة البشريّة، وخرق حوسبة النّقل للقيود الجزبريّة التي أقرّتها النّظريّة اللّسانيّة، وهو ما يجعلنا نشكّك في كفايته الوصفيّة لسانيّا.
We unify recent neural approaches to one-shot learning with older ideas of associative memory in a model for metalearning. Our model learns jointly to represent data and to bind class labels to representations in a single shot. It builds representations via slow weights, learned across tasks through SGD, while fast weights constructed by a Hebbian learning rule implement one-shot binding for each new task. On the Omniglot, Mini-ImageNet, and Penn Treebank one-shot learning benchmarks, our model achieves state-of-the-art results.
We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+% absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental results on parsing 61 "big" Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Straková, 2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at: https://github.com/datquocnguyen/jPTDP
Failing to recognize one's mirror image can signal an abnormality in one's sense of self. In dissociative identity disorder (DID), individuals often report that their mirror image can feel unfamiliar or distorted. They also experience some of their own thoughts, emotions, and bodily sensations as if they are nonautobiographical and sometimes as if instead, they belong to someone else. To assess these experiences, we designed a novel backwards masking paradigm in which participants were covertly shown their own face, masked by a stranger's face. Participants rated feelings of familiarity associated with the strangers' faces. 21 control participants without trauma-generated dissociation rated masks, which were covertly preceded by their own face, as more familiar compared to masks preceded by a stranger's face. In contrast, across two samples, 28 individuals with DID and similar clinical presentations (DSM-IV Dissociative Disorder Not Otherwise Specified type 1) did not show increased familiarity ratings to their own masked face. However, their familiarity ratings interacted with self-reported identity state integration. Individuals with higher levels of identity state integration had response patterns similar to control participants. These data provide empirical evidence of aberrant self-referential processing in DID/DDNOS and suggest this is restored with identity state integration.
The first edition of one of the most important and mysterious novels of the 20th century appeared more than fifty years ago. Despite the passage of time The Master and Margarita still enjoys popularity; it also intrigues and inspires. Until now five Polish translations of Bulgakov’s novel have appeared. It is known that the interpretation of the original might be expressed in the form of many potential texts that are communicatively equivalent. There is no doubt that it is the translator who plays a vital role in any translation; her/his personality, life experience, knowledge, skills, and also the times s/he lives in regulate the target text. That is why, no matter how many times a text is translated, the final product will always be different. Taking this into consideration, the author will compare the three Polish translations of Bulgakov’s Master and Margarita, paying attention to the diachronic perspective as far as linguistic norms are concerned, the modernity of language, and the way the anthroponyms are expressed.
Are gender differences in emotion culturally universal? To answer this question, the current study compared gender differences in emotional arousal (intensity) ratings for negative and positive pictures from the International Affective Picture System (IAPS) across cultures (Chinese vs. German culture) and age (younger vs. older adults). The raters were 53 younger Germans (24 women), 53 older Germans (28 women), 300 younger Chinese (176 women), and 126 older Chinese (86 women). The results showed that gender differences in arousal ratings were moderated by culture and age: Chinese women reported higher arousal for both negative and positive pictures compared with Chinese men; German women reported higher arousal for negative pictures, but lower arousal for positive pictures compared with German men. Moreover, the gender differences were larger for older than younger adults in the Chinese sample but smaller for older than younger adults in the German sample. The results indicated that gender differences in self-report emotional intensity induced by pictorial stimuli were more consistent with gender norms and stereotypes (i.e., women being more emotional than men) in the Chinese sample, compared with the German sample, and that gender differences were not constant across age groups. The study revealed that gender differences in emotion are neither constant nor universal, and it highlighted the importance of taking culture and age into account.
This paper describes a transduction language suitable for natural language treebank transformations and motivates its application to tasks that have been used and described in the literature. The language, which is the basis for a tree transduction tool allows for clean, precise and concise description of what has been very confusingly, ambiguously, and incompletely textually described in the literature also allowing easy non-hard-coded implementation. We also aim at getting feedback from the NLP community to eventually converge to a de facto standard for such transduction language.
Summary: Standard Catalan is based on the Central dialect and, specifically, on Barcelona speech. However, there are standard variants for all dialects, except for the Northern one. Furthermore, the sociolinguistic situation in Northern Catalan differs from that in other Catalan-speaking territories in that the language has almost disappeared. Some cultural activists are still trying to recover the Catalan language by using it in as many situations as possible. The objective of this article is to analyse the variety of Catalan – standard or dialectal forms – used in literature, the media, and education and what this usage demonstrates about Northern Catalans’ attitudes towards their own language. Keywords: Northern Catalan, standard Catalan, sociolinguistics, language attitudes
We present the Uppsala system for the CoNLL 2018 Shared Task on universal\ndependency parsing. Our system is a pipeline consisting of three components:\nthe first performs joint word and sentence segmentation; the second predicts\npart-of- speech tags and morphological features; the third predicts dependency\ntrees from words and tags. Instead of training a single parsing model for each\ntreebank, we trained models with multiple treebanks for one language or closely\nrelated languages, greatly reducing the number of models. On the official test\nrun, we ranked 7th of 27 teams for the LAS and MLAS metrics. Our system\nobtained the best scores overall for word segmentation, universal POS tagging,\nand morphological features.\n
Au début de cette thèse, aucun corpus annoté syntaxiquement (treebank) n’était disponible pour le serbe. Or, les treebanks annotés manuellement sont une condition sine qua non du développement (entraînement et évaluation) d’outils statistiques dédiés à l’annotation syntaxique automatique (parsers). L’existence des parsers performants permet à son tour l’annotation syntaxique de corpus plus larges, qui peuvent ensuite alimenter des recherches en linguistique théorique. De fait, l’absence de ces ressources pour le serbe freine le développement des recherches sur cette langue dans ces deux directions, et plus généralement les efforts visant l’informatisation et la valorisation du serbe. Afin de combler cette lacune, nous avons constitué un ensemble de ressources pour le traitement automatique du serbe. Il s’agit en premier lieu du treebank ParCoTrain-Synt, qui contient 101 000 tokens annotés en morphosyntaxe, en lemmes et en syntaxe de dépendances. Nous avons également confectionné le lexique ParCoLex, doté de 7 millions d’entrées provenant de 157 000 lemmes différents. En exploitant ces deux ressources, nous avons développé des modèles pour le parsing, pour l’étiquetage et pour la lemmatisation.Toutes les ressources citées sont librement diffusées à l’adresse suivante: https://github.com/aleksandra-miletic/serbian-nlp-resources. Les ressources constituées ont également été exploitées dans le cadre de deux études linguistiques, montrant ainsi que le corpus ParCoTrain-Synt ouvre la porte aux études empiriques basées sur des analyses quantitatives dans le domaine de la linguistique serbe.
Our study aims to explore how much information about areal patterns of colexification we can gain from lexical databases such as CLICS and ASJP. We adopt a bottom-up (rather than hypothesis-driven) approach, identifying areal patterns in three steps: (i) determine spatial autocorrelations in the data, (ii) identify clusters as candidates for convergence areas and (iii) test the clusters resulting from the second step controlling for genealogical relatedness. Moreover, we identify a (genealogical) diversity index for each cluster. This approach yields promising results, which we regard as a proof of concept, but we also point out some drawbacks of the use of major lexical databases.
Modern solutions for implicit discourse relation recognition largely build universal models to classify all of the different types of discourse relations. In contrast to such learning models, we build our model from first principles, analyzing the linguistic properties of the individual top-level Penn Discourse Treebank (PDTB) styled implicit discourse relations: Comparison, Contingency and Expansion. We find semantic characteristics of each relation type and two cohesion devices---topic continuity and attribution---work together to contribute such linguistic properties. We encode those properties as complex features and feed them into a NaiveBayes classifier, bettering baselines(including deep neural network ones) to achieve a new state-of-the-art performance level. Over a strong, feature-based baseline, our system outperforms one-versus-other binary classification by 4.83% for Comparison relation, 3.94% for Contingency and 2.22% for four-way classification.
The transfer or share of knowledge between languages is a popular solution to resource scarcity in NLP. However, the effectiveness of cross-lingual transfer can be challenged by variation in syntactic structures. Frameworks such as Universal Dependencies (UD) are designed to be cross-lingually consistent, but even in carefully designed resources trees representing equivalent sentences may not always overlap. In this paper, we measure cross-lingual syntactic variation, or anisomorphism, in the UD treebank collection, considering both morphological and structural properties. We show that reducing the level of anisomorphism yields consistent gains in cross-lingual transfer tasks. We introduce a source language selection procedure that facilitates effective cross-lingual parser transfer, and propose a typologically driven method for syntactic tree processing which reduces anisomorphism. Our results show the effectiveness of this method for both machine translation and cross-lingual sentence similarity, demonstrating the importance of syntactic structure compatibility for boosting cross-lingual transfer in NLP.
The purpose of this study was to evaluate the relationship between emotional responses to sounds, hearing acuity, and isolation, specifically objective isolation (social disconnectedness) and subjective isolation (loneliness). It was predicted that ratings of valence in response to pleasant and unpleasant stimuli would influence the relationship between hearing loss and isolation. Participants included 83 adults, without depression, who were categorized into three groups (young with normal hearing, older with normal hearing, and adults with mild-to-moderately severe hearing loss). Participants made ratings of valence in response to pleasant and unpleasant nonspeech sounds, presented at a moderate overall level in the laboratory. Participants also completed questionnaires related to social disconnectedness and loneliness. Data were analyzed using multiple regression with questionnaire scores as dependent variables. Independent variables were age, gender, degree of hearing loss, perceived hearing handicap, number of depressive symptoms, mean valence rating in response to unpleasant sounds, and mean valence rating in response to pleasant sounds. Emotional responses to pleasant sounds explained significant variability in scores of both social disconnectedness and loneliness. Depressive symptoms also explained variability in loneliness scores. Hearing loss was not significantly related to social disconnectedness or loneliness, although it was the only variable significantly related to ratings of valence in response to pleasant sounds. Emotional responses to pleasant sounds are related to disconnectedness and loneliness. Although not related to isolation in this study, hearing loss was related to emotional responses. Thus, emotional responses should be considered in future models of isolation and hearing loss.
The present study features speech errors Italian students make in translation as the most complex form of speech activity. The examined speech errors were made by both students at the Higher School of Translation and budding traslators. The low quality of technical translation and the large number of translation errors determined the scope of the present research: to draw the scientists’ attention to the problem in question, to generalize the translation practice, and to work out recommendations on preventing translation errors. The research employed general scientific methods (generalization, analysis, synthesis) and empirical research methods. The author puts forward some possible causes of error making, e.g. interlingual and cross-language interference, loan translations, etc. The author gives examples of the most typical student errors, indicates the specific reasons behind them, and offers recommendations for error prevention. The results of this study can be used for training and practical purposes. The author comes to the conclusion that it is impossible not to make errors due to the fact that the students cannot overcome interference while in their native linguistic environment. However, it is quite possible to reduce the number of errors. The article is composed of three parts. The introduction specifies the aims and tasks of the work. The main body of the article introduces some specific examples of student errors. In the conclusion, the author sums up recommendations for effective work in the learning process.
Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inability to effectively visualize these relationships has led to DNNs being characterized as black boxes and consequently limited their applications. To ameliorate this problem, we introduce the use of hierarchical interpretations to explain DNN predictions through our proposed method, agglomerative contextual decomposition (ACD). Given a prediction from a trained DNN, ACD produces a hierarchical clustering of the input features, along with the contribution of each cluster to the final prediction. This hierarchy is optimized to identify clusters of features that the DNN learned are predictive. Using examples from Stanford Sentiment Treebank and ImageNet, we show that ACD is effective at diagnosing incorrect predictions and identifying dataset bias. Through human experiments, we demonstrate that ACD enables users both to identify the more accurate of two DNNs and to better trust a DNN's outputs. We also find that ACD's hierarchy is largely robust to adversarial perturbations, implying that it captures fundamental aspects of the input and ignores spurious noise.
In this paper, we propose Dynamic Self-Attention (DSA), a new self-attention mechanism for sentence embedding. We design DSA by modifying dynamic routing in capsule network (Sabouretal.,2017) for natural language processing. DSA attends to informative words with a dynamic weight vector. We achieve new state-of-the-art results among sentence encoding methods in Stanford Natural Language Inference (SNLI) dataset with the least number of parameters, while showing comparative results in Stanford Sentiment Treebank (SST) dataset.
Past studies examining how people judge faces for trustworthiness and dominance have suggested that they use particular facial features (e.g. mouth features for trustworthiness, eyebrow and cheek features for dominance ratings) to complete the task. Here, we examine whether eye movements during the task reflect the importance of these features. We here compared eye movements for trustworthiness and dominance ratings of face images under three stimulus configurations: Small images (mimicking large viewing distances), large images (mimicking face to face viewing), and a moving window condition (removing extrafoveal information). Whereas first area fixated, dwell times, and number of fixations depended on the size of the stimuli and the availability of extrafoveal vision, and varied substantially across participants, no clear task differences were found. These results indicate that gaze patterns for face stimuli are highly individual, do not vary between trustworthiness and dominance ratings, but are influenced by the size of the stimuli and the availability of extrafoveal vision.
В статье представлен обзор исследований, посвященных проблеме территориальной диф- ференциации языка в ортологическом аспекте. Установлено, что в связи с языковой нормой лин- гвисты выделяют три типа регионального варьирования: 1) сосуществование отдельных локаль- но маркированных единиц в одной нормативной системе; 2) дивергенцию диатопических орто- логических комплексов; 3) взаимодействие нормативных реализаций с их ненормативными диалектными аналогами в рамках определенного национального языка. Особое внимание уделя- ется ортологическому подходу к изучению регионального варьирования в отечественной лин- гвистике, а также особенностям использования локализмов в современной российской массовой коммуникации.
The repertoire of forms of address can be considered as one of the determinants of the discourse genre, which makes it possible to capture its evolution and cultural variations. From such comparative, intra- and intercultural perspective, adopting an interactive approach in the analysis of political discourse, we will look at the practice of addressing one another in the French and Polish politicalmedia discourse. While in both languages the linguistic norm recommends the use of the polite forms of address in official situations, the cases of the use of the familiar pronoun tu / ty in media interactions between politicians are not rare at all. Whether it is an informal talk of politicians caught by the media, a television pre-election debate, or a meeting of the heads of state, addressing the other person by the familiar forms is a manifestation of a deliberate blurring of the boundaries between the front-stage and backstage in political discourse in order to create the impression of intimacy andequality between the interlocutors.
In this paper, we describe our project of building a phrase structure treebank for Persian. The treebank consists of approximately 30000 sentences. With the help of this treebank, the researcher can investigate syntactic phenomena, extract grammars, train and test parsers, etc. In addition to these motivations, as another advantage of it we can refer to the fact that the sentences of this treebank are selected from an available dependency treebank. So the final treebank has two syntactic representations: phrase structure and dependency structure. The treebank is built using a bootstrapping approach, which converts a dependency structure tree to a phrase structure tree and the annotations are corrected manually. Using the new phrase structure treebank, we train models for constituency parsers. The treebank is freely available for educational purposes <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.
We evaluate corpus-based measures of linguistic complexity obtained using Universal Dependencies (UD) treebanks. We propose a method of estimating robustness of the complexity values obtained using a given measure and a given treebank. The results indicate that measures of syntactic complexity might be on average less robust than those of morphological complexity. We also estimate the validity of complexity measures by comparing the results for very similar languages and checking for unexpected differences. We show that some of those differences that arise can be diminished by using parallel treebanks and, more importantly from the practical point of view, by harmonizing the languagespecific solutions in the UD annotation.
We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-to-graph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual availability of an AMR-to-text generator. The framework is data-driven, trainable, and not specifically designed for a particular domain. Experiments on gold-standard AMR annotations and system parses show promising results. Code is available at: https://github.com/summarization
We explore dynamic evaluation, where sequence models are adapted to the recent sequence history using gradient descent, assigning higher probabilities to re-occurring sequential patterns. We develop a dynamic evaluation approach that outperforms existing adaptation approaches in our comparisons. We apply dynamic evaluation to outperform all previous word-level perplexities on the Penn Treebank and WikiText-2 datasets (achieving 51.1 and 44.3 respectively) and all previous character-level cross-entropies on the text8 and Hutter Prize datasets (achieving 1.19 bits/char and 1.08 bits/char respectively).
Chronic pain may alter both affect- and value-related behaviors, which represents a potentially treatable aspect of chronic pain experience. Current understanding of how chronic pain influences the function of brain reward systems, however, is limited. Using a monetary incentive delay task and functional magnetic resonance imaging (fMRI), we measured neural correlates of reward anticipation and outcomes in female participants with the chronic pain condition of fibromyalgia (N = 17) and age-matched, pain-free, female controls (N = 15). We hypothesized that patients would demonstrate lower positive arousal, as well as altered reward anticipation and outcome activity within corticostriatal circuits implicated in reward processing. Patients demonstrated lower arousal ratings as compared with controls, but no group differences were observed for valence, positive arousal, or negative arousal ratings. Group fMRI analyses were conducted to determine predetermined region of interest, nucleus accumbens (NAcc) and medial prefrontal cortex (mPFC), responses to potential gains, potential losses, reward outcomes, and punishment outcomes. Compared with controls, patients demonstrated similar, although slightly reduced, NAcc activity during gain anticipation. Conversely, patients demonstrated dramatically reduced mPFC activity during gain anticipation-possibly related to lower estimated reward probabilities. Further, patients demonstrated normal mPFC activity to reward outcomes, but dramatically heightened mPFC activity to no-loss (nonpunishment) outcomes. In parallel to NAcc and mPFC responses, patients demonstrated slightly reduced activity during reward anticipation in other brain regions, which included the ventral tegmental area, anterior cingulate cortex, and anterior insular cortex. Together, these results implicate altered corticostriatal processing of monetary rewards in chronic pain.
The Effective Set-Size model has been used to describe uncertainty in various signal detection experiments. The model regards images as if they were an effective number (M*) of searchable locations, where the observer treats each location as a location-known-exactly detection task with signals having average detectability d'. The model assumes a rational observer behaves as if he searches an effective number of independent locations and follows signal detection theory at each location. Thus the location-known-exactly detectability (d') and the effective number of independent locations M* fully characterize search performance. In this model the image rating in a single-response task is assumed to be the maximum response that the observer would assign to these many locations. The model has been used by a number of other researchers, and is well corroborated. We examine this model as a way of differentiating imaging tasks that radiologists perform. Tasks involving more searching or location uncertainty may have higher estimated M* values. In this work we applied the Effective Set-Size model to a number of medical imaging data sets. The data sets include radiologists reading screening and diagnostic mammography with and without computer-aided diagnosis (CAD), and breast tomosynthesis. We developed an algorithm to fit the model parameters using two-sample maximum-likelihood ordinal regression, similar to the classic bi-normal model. The resulting model ROC curves are rational and fit the observed data well. We find that the distributions of M* and d' differ significantly among these data sets, and differ between pairs of imaging systems within studies. For example, on average tomosynthesis increased readers’ d' values, while CAD reduced the M* parameters. We demonstrate that the model parameters M* and d' are correlated. We conclude that the Effective Set-Size model may be a useful way of differentiating location uncertainty from the diagnostic uncertainty in medical imaging tasks.
Activities can increase quality of life for residents with dementia, however determining which activities are high in quality is often subjective. In this study, trained researchers observed 22 residents in common areas of a memory care unit (10 males, 12 female, all consented for research). Assessments occurred in 15-minute sessions across multiple days (totaling 7000 minutes). Each minute involved co-observation of staff interactions (Quality Interaction Scale, Dean, et al., 1993) and residents’ positive affect (Philadelphia Geriatric Center Affect Rating Scale, Lawton, et al., 1996). Observers noted types of activities underway. Z-scores indicated proportionally higher positive affect in residents during preplanned activities, compared to non-facilitated/unplanned activities (z = -3.09, p <.001). Compared to residents’ positive affect during “no activity”, positive affect was proportionally highest (p <.001) during music therapy (z = -23.43) and motor activity (z = -13.67), and lowest (p = n.s.) during dance performances (z = -1.07) and art/crafts (z = 1.83). Compared to positive staff interactions during “no activity”, positive staff interactions was proportionally highest (p <.001) during motor activities (z = -12.74) and music therapy (z = -11.86), and lowest (p = n.s.) during cognitive activities (z = -0.30) and music presentations (z = -0.17). Commonalities in quality activities included residents being able to see, engage, and move about if they wanted, staff considering residents’ autonomy and staff using active efforts to converse with residents. Staff can observe affect in residents to evaluate engagement during activities, and adjust delivery and interaction frequency where needed.
Previous studies have demonstrated differential perception of body expressions between males and females. However, only two recent studies (Kret et al., 2011; Krüger et al., 2013) explored the interaction effect between observer gender and subject gender, and it remains unclear whether this interaction between the two gender factors is gender-congruent (i.e., better recognition of emotions expressed by subjects of the same gender) or gender-incongruent (i.e., better recognition of emotions expressed by subjects of the opposite gender). Here, we used event-related potentials (ERPs) to investigate the recognition of fearful and angry body expressions posed by males and females. Male and female observers also completed an affective rating task (including valence, intensity, and arousal ratings). Behavioral results showed that male observers reported higher arousal rating scores for angry body expressions posed by females than males. ERP data showed that when recognizing angry body expressions, female observers had larger P1 for male than female bodies, while male observers had larger P3 for female than male bodies. These results indicate gender-incongruent effects in early and later stages of body expression processing, which fits well with the evolutionary theory that females mainly play a role in care of offspring while males mainly play a role in family guarding and protection. Furthermore, it is found that in both angry and fearful conditions male observers exhibited a larger N170 for male than female bodies, and female observers showed a larger N170 for female than male bodies. This gender-incongruent effect in the structural encoding stage of processing may be due to the familiarity of the body configural features of the same gender. The current results provide insights into the significant role of gender in body expression processing, helping us understand the issue of gender vulnerability associated with psychiatric disorders characterized by deficits of body language reading.
Recent work on the problem of latent tree learning has made it possible to train neural networks that learn to both parse a sentence and use the resulting parse to interpret the sentence, all without exposure to ground-truth parse trees at training time. Surprisingly, these models often perform better at sentence understanding tasks than models that use parse trees from conventional parsers. This paper aims to investigate what these latent tree learning models learn. We replicate two such models in a shared codebase and find that (i) only one of these models outperforms conventional tree-structured models on sentence classification, (ii) its parsing strategies are not especially consistent across random restarts, (iii) the parses it produces tend to be shallower than standard Penn Treebank (PTB) parses, and (iv) they do not resemble those of PTB or any other semantic or syntactic formalism that the authors are aware of.
Released only a year ago as the outputs of a research project (``Parsing Web 2.0 Sentences'', supported in part by a TÜBİTAK 1001 grant (No. 112E276) and a part of the ICT COST Action PARSEME (IC1207)), IMST and IWT are currently the most comprehensive Turkish dependency treebanks in the literature. This article introduces the final states of our treebanks, as well as a newly integrated hierarchical categorization of the multiheaded dependencies and their organization in an exclusive deep dependency layer in the treebanks. It also presents the adaptation of recent studies on standardizing multiword expression and named entity annotation schemes for the Turkish language and integration of benchmark annotations into the dependency layers of our treebanks and the mapping of the treebanks to the latest Universal Dependencies (v2.0) standard, ensuring further compliance with rising universal annotation trends. In addition to significantly boosting the universal recognition of Turkish treebanks, our recent efforts have shown an improvement in their syntactic parsing performance (up to 77.8{\%}/82.8{\%} LAS and 84.0{\%}/87.9{\%} UAS for IMST/IWT, respectively). The final states of the treebanks are expected to be more suited to different natural language processing tasks, such as named entity recognition, multiword expression detection, transfer-based machine translation, semantic parsing, and semantic role labeling.
This paper presents results from the first statistical dependency parser for Turkish. Turkish is a free-constituent order language with complex agglutinative inflectional and derivational morphology and presents interesting challenges for statistical parsing, as in general, dependency relations are between “portions” of words – called inflectional groups. We have explored statistical models that use different representational units for parsing. We have used the Turkish Dependency Treebank to train and test our parser but have limited this initial exploration to that subset of the treebank sentences with only left-to-right non-crossing dependency links. Our results indicate that the best accuracy in terms of the dependency relations between inflectional groups is obtained when we use inflectional groups as units in parsing, and when contexts around the dependent are employed.
Introduction. The article explores the impact of various types of verbal representation of ethnic stereotypes in the framework of a polyethnical academic community, i.e. educational environment in modern international university. Although the educational process with subjects of different cultural backgrounds plays a crucial role in conveying world views of representatives of different cultures, the research on the linguistic representation of stereotyped views on representatives of other nationalities has not been conducted yet. This aspect determines the relevance of the study. The aim of the research is to compare the impact levels of purely linguistic and speech ways of verbalising heterostereotypes by ways of employing relevant linguistic data for academic purposes during foreign language classes. Materials and Methods. The first stage of the experiment resulted in preparation of the linguistic corpus for the research: by means of comprehensive vocabulary research the lexical database with ethnonyms or ethnonym-based adjectives was compiled. To reveal the potential of their usage in the education processes, the participants were offered the preliminary and final surveys held as free associatio n experiment. Results. The influential potential for purely linguistic and speech ways of representing national stereotypes was compared to find out if they relate to the descriptors and scripts revealed through analysis of phraseological units and national anecdote respectively, while the latter was marked as a more efficient way of delivering ethnic stereotypes. The conclusions based on the analysis of the data obtained were drawn on how to use relevant linguistic material for academic purposes in order to appropriately develop attitudes to other ethnic groups. Discussion and Conclusions. The conducted research revealed more significant impact degree for ethnic anecdotes against investigation of lexical-phraseological units containing ethnonyms or ethnonym-based adjectives. It was illustrated by collection and further analysis of verbal reactions provided by students of non-linguistic departments of the modern University who took part in the preliminary and final stages which were in line with the beginning and end of the academic term accordingly. The portraits of typical national representatives made by the students at the completion of the course which included sessions on studying dictionary extracts and national anecdotes, to a greater extent conformed with the stereotypes delivered by ethnic anecdotes than the linguistic corpus of lexical-phraseological units. The research results may be considered during the development of the curriculum for foreign language courses in international universities with polyethnical academic environment.
High quality communication between health care providers (HCPs) and adolescents and young adults (AYAs) with type 1 diabetes (T1D) may contribute to better diabetes self-care and health outcomes. Health communication reflects both informational content and how information is conveyed, including affect and tone. The aim of this study was to assess HCP affective communication and the relationship between HCP affective communication and glycemic control in AYAs with T1D. As part of a larger study of AYA-HCP health communication, routine clinic visits for 69 AYAs with T1D (M age 17.81 years; 56.5% female) and 8 HCPs (88% female) were audiorecorded. Clinic visits were coded using the Roter Interaction Analysis System (RIAS), a validated coding structure assessing verbal and non-verbal exchanges in a medical encounter. HCP global affective ratings were used to create two composite variables—positive HCP affect (e.g., attentiveness; respectfulness; Cronbach’s a = 0.82) and negative HCP affect (e.g., anger; dominance; Cronbach’s a = 0.75). Hemoglobin A1c (A1c) was taken from the medical chart. The mean A1c was 8.97% (±2.30). Descriptive analyses of positive and negative HCP affect indicated that HCPs expressed a high level of positive affect (M = 4.21) and a relatively low level of negative affect (M = 2.80). Negative affect was positively associated with HbA1c. After controlling for salient covariates (e.g., HCP, race, regimen), A1c accounted for a significant portion of the variance in negative affect during the clinic visit (Adj R2 =.36, ß = 0.57, p &lt; 0.001). This sample of HCPs predominantly exhibited positive affect during routine T1D visits. Glycemic control was not associated with positive affect, but higher A1c was associated with more negative affect. This finding suggests elevated A1c levels may elicit more negative affect in routine diabetes care. Future research should examine these associations over time, including how AYA-HCP health communication quality predicts long-term glycemic control. Disclosure K. Homma: None. F.R. Cogen: None. R. Streisand: None. M. Monaghan: Research Support; Self; American Diabetes Association, National Institutes of Health.
Socioemotional Selectivity Theory posits that as person progresses through the life cycle, he or she makes concerted steps to maximize social and emotional wellbeing through selective patterns of emotional processing (Carstensen, 1995). The temporal positioning of an individual’s goals, motivations, and social orientations, or future time perspective (FTP), drives this change in emotional processing. The association between FTP and age is a naturally-occurring phenomenon as FTP becomes more limited as a person ages; however, it is believed that the construct of future time perspective is sufficiently malleable to be experimentally manipulated (Carstensen, 2003). The current study assessed the effects of a future time perspective manipulation on the emotional processing of positively and negatively valenced IAPS images in a college sample. Emotional processing was indexed by heart rate variability (HRV), skin conductance, memory recall, and eye-tracking. Young adult volunteers (N=22) were randomly assigned to one of two experimental conditions, wherein their future time perspective was manipulated to become either more limited or more expansive. Participants viewed a series of positive and negative cues followed by corresponding valenced images before and after the future time perspective manipulation. Preliminary results of the current study suggest the imagery task had no significant effect on FTP. Due to limitations from the small sample size, a larger sample size will be needed to conduct valid group comparisons to sufficiently test the effectiveness of the manipulation. Results of this study show participants with a more limited FTP had lower LF and greater HF HRV, indicating greater emotional regulation of arousal during the task. Interestingly, our results also indicate that positive affect ratings on the PANAS were related to avoiding negative emotional content (lower fixation percentage for negative images and cues), remembering more positive information (greater positive memory recall), \ndetecting a greater saliency for positive information (longer skin conductance rec t/2), lower sympathetic activity (lower posttest LF and SCL) and greater parasympathetic activity (greater posttest HF and RMSSD). These data suggest that reports of affect might provide more sensitive indication of emotional processing than future time perspective.
Blueberries have been reported to possess several anti-inflammatory properties. Previous studies examining the anti-inflammatory effect of blueberries on acute inflammation caused by exercise-induced muscle damage are largely inconclusive. This may be due to the dose used in these studies not accounting for an individual’s lean mass (LM), the compartment directly involved during exercise, when determining appropriate blueberry dosage. PURPOSE: To examine the effect of blueberry supplementation (BB) at a dose relative to LM on delayed onset muscle soreness (DOMS) and recovery. METHODS: Fourteen recreationally active women (age: 21±1yr; body fat: 24.8±4.5%) participated in this double blind, matched-pairs study. Participants were matched by LM and randomly assigned to either a BB or a placebo (PLA) group. Leg strength was assessed via one-repetition maximum (1RM) on a leg press. Participants consumed a daily dose of freeze-dried BB powder (1.6g BB/kgLM) or a PLA (1.6g PLA/kgLM) for 7 days prior to induction of DOMS. Participants completed 6 sets of 10 repetitions at 70% 1RM on the leg press to induce DOMS. Perceived soreness (questionnaire), pressure-pain threshold (dolorimeter), and average power (AP; Biodex™) of the right thigh muscles were assessed immediately before (PRE) and after (POST), 24, 48, and 72h post induction of DOMS. Repeated measures ANOVAs were used for analyses. Significance was set at p<0.05. RESULTS: There were no group x time interactions for perceived soreness, pressure-pain threshold, and AP, however, significant time effects were observed for these variables. When comparing pre to post 24hr (p<0.001), 48hr (p=0.001), and 72hr (p=0.011) perceived soreness of the thigh muscles significantly increased. Pressure-pain threshold of the thigh muscles decreased significantly from pre to post 24hr (p=0.023), 48hr (p=0.001), and 72hr (p=0.024. Isokinetic leg extension AP decreased from pre to post 24hr (BB: 83±17 to 76±22Nm; PLA: 85±21 to 79±26Nm; p=0.02). CONCLUSION: Consumption of BB for 7 days prior to DOMS induction on a leg press does not affect rating of perceived soreness, pain threshold, nor attenuate decreases in performance compared to a PLA in recreationally active women.
This paper describes our system (SLT-Interactions) for the CoNLL 2018 shared task: Multilingual Parsing from Raw Text to Universal Dependencies. Our system performs three main tasks: word segmentation (only for few treebanks), POS tagging and parsing. While segmentation is learned separately, we use neural stacking for joint learning of POS tagging and parsing tasks. For all the tasks, we employ simple neural network architectures that rely on long short-term memory (LSTM) networks for learning task-dependent features. At the basis of our parser, we use an arc-standard algorithm with Swap action for general non-projective parsing. Additionally, we use neural stacking as a knowledge transfer mechanism for cross-domain parsing of low resource domains. Our system shows substantial gains against the UDPipe baseline, with an average improvement of 4.18% in LAS across all languages. Overall, we are placed at the 12 th position on the official test sets.
The article presents a quantitative analysis of some syntactic dependency properties in Czech. A dependency frame is introduced as a linguistic unit and its characteristics are investigated. In particular, a ranked frequencies of dependency frames are observed and modelled and a relationship between particular syntactic functions and the number of dependency frames is examined. For the analysis, the Czech Universal Dependency Treebank is used.
In literature, writers have the liberty to deviate from linguistic norms under a principle known as poetic license. Poetic license allows deviation in favour of making language inspiring. Deviation from linguistic norms often implies that writers can take liberties with word formation, thus neology in literary contexts should be addressed specifically. This article analyses the status of literary coinages in the scope of neology and describes the specific context of children’s literature. The article also offers a typology of nonce formation processes for occasionalisms, with textual analysis, from a corpus of children’s books, using J. Tournier’s matrices of lexicogenesis [2007: 51].
This paper presents a truly full character-level neural dependency parser together with a newly released character-level dependency treebank for Chinese, which has suffered a lot from the dilemma of defining word or not to model character interactions. Integrating full character-level dependencies with character embedding and human annotated character-level part-of-speech and dependency labels for the first time, we show an extra performance enhancement from the evaluation on Chinese Penn Treebank and SJTU (Shanghai Jiao Tong University) Chinese Character Dependency Treebank and the potential of better understanding deeper structure of Chinese sentences.
To approximately parse an unfamiliar language, it helps to have a treebank of a similar language. But what if the closest available treebank still has the wrong word order? We show how to (stochastically) permute the constituents of an existing dependency treebank so that its surface part-of-speech statistics approximately match those of the target language. The parameters of the permutation model can be evaluated for quality by dynamic programming and tuned by gradient descent (up to a local optimum). This optimization procedure yields trees for a new artificial language that resembles the target language. We show that delexicalized parsers for the target language can be successfully trained using such "made to order" artificial languages.
As the amount of unstructured text data that humanity produces overall and on the Internet grows, so does the need to intelligently to process it and extract different types of knowledge from it. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to natural language processing systems with comparative, remarkable results. The CNN is a noble approach to extract higher level features that are invariant to local translation. However, it requires stacking multiple convolutional layers in order to capture long-term dependencies, due to the locality of the convolutional and pooling layers. In this paper, we describe a joint CNN and RNN framework to overcome this problem. Briefly, we use an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning network, then, the pre-trained parameters of the network are used to initialize the model. At a final stage, the proposed framework combines former information with a set of feature maps learned by a convolutional layer with long-term dependencies learned via long-short-term memory. Empirically, we show that our approach, with slight hyperparameter tuning and static vectors, achieves outstanding results on multiple sentiment analysis benchmarks. Our approach outperforms several existing approaches in term of accuracy; our results are also competitive with the state-of-the-art results on the Stanford Large Movie Review data set with 93.3% accuracy, and the Stanford Sentiment Treebank data set with 48.8% fine-grained and 89.2% binary accuracy, respectively. Our approach has a significant role in reducing the number of parameters and constructing the convolutional layer followed by the recurrent layer as a substitute for the pooling layer. Our results show that we were able to reduce the loss of detailed, local information and capture long-term dependencies with an efficient framework that has fewer parameters and a high level of performance.
Recently, researchers have developed black-box approaches to mine design and interaction data from mobile apps. Although the data captured during this interaction mining is descriptive, it does not expose the design semantics of UIs: what elements on the screen mean and how they are used. This paper introduces an automatic approach for generating semantic annotations for mobile app UIs. Through an iterative open coding of 73k UI elements and 720 screens, we contribute a lexical database of 25 types of UI components, 197 text button concepts, and 135 icon classes shared across apps. We use this labeled data to learn code-based patterns to detect UI components and to train a convolutional neural network that distinguishes between icon classes with 94% accuracy. To demonstrate the efficacy of our approach at scale, we compute semantic annotations for the 72k unique UIs in the Rico dataset, assigning labels for 78% of the total visible, non-redundant elements.
We introduce a novel architecture for dependency parsing: stack-pointer networks (STACKPTR). Combining pointer networks The stack tracks the status of the depthfirst search and the pointer networks select one child for the word at the top of the stack at each step. The STACKPTR parser benefits from the information of the whole sentence and all previously derived subtree structures, and removes the leftto-right restriction in classical transitionbased parsers. Yet, the number of steps for building any (including non-projective) parse tree is linear in the length of the sentence just as other transition-based parsers, yielding an efficient decoding algorithm with O(n 2 ) time complexity. We evaluate our model on 29 treebanks spanning 20 languages and different dependency annotation schemas, and achieve state-of-theart performance on 21 of them.
In this paper, we propose RNN-Capsule, a capsule model based on Recurrent Neural Network (RNN) for sentiment analysis. For a given problem, one capsule is built for each sentiment category e.g., 'positive' and 'negative'. Each capsule has an attribute, a state, and three modules: representation module, probability module, and reconstruction module. The attribute of a capsule is the assigned sentiment category. Given an instance encoded in hidden vectors by a typical RNN, the representation module builds capsule representation by the attention mechanism. Based on capsule representation, the probability module computes the capsule's state probability. A capsule's state is active if its state probability is the largest among all capsules for the given instance, and inactive otherwise. On two benchmark datasets (i.e., Movie Review and Stanford Sentiment Treebank) and one proprietary dataset (i.e., Hospital Feedback), we show that RNN-Capsule achieves state-of-the-art performance on sentiment classification. More importantly, without using any linguistic knowledge, RNN-Capsule is capable of outputting words with sentiment tendencies reflecting capsules' attributes. The words well reflect the domain specificity of the dataset.
We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-ofthe-art discriminative constituency parser. The use of attention makes explicit the manner in which information is propagated between different locations in the sentence, which we use to both analyze our model and propose potential improvements. For example, we find that separating positional and content information in the encoder can lead to improved parsing accuracy. Additionally, we evaluate different approaches for lexical representation. Our parser achieves new state-ofthe-art results for single models trained on the Penn Treebank: 93.55 F1 without the use of any external data, and 95.13 F1 when using pre-trained word representations. Our parser also outperforms the previous best-published accuracy figures on 8 of the 9 languages in the SPMRL dataset.