Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
The ventromedial and dorsolateral prefrontal cortex are two major prefrontal regions that usually interact in serving different cognitive functions. On the other hand, these regions are also involved in cognitive processing of emotions but their contribution to emotional processing is not well-studied. In the present study, we investigated the role of these regions in three dimensions (valence, arousal and dominance) of emotional processing of stimuli via ratings of visual stimuli performed by the study participants on these dimensions. Twenty- two healthy adult participants (mean age 25.21 ± 3.84 years) were recruited and received anodal and sham transcranial direct current stimulation (tDCS) (1.5 mA, 15 min) over the dorsolateral prefrontal cortex (dlPFC) and and ventromedial prefrontal cortex (vmPFC) in three separate sessions with an at least 72-h interval. During stimulation, participants underwent an emotional task in each stimulation condition. The task included 100 visual stimuli and participants were asked to rate them with respect to valence, arousal, and dominance. Results show a significant effect of stimulation condition on different aspects of emotional processing. Specifically, anodal tDCS over the dlPFC significantly reduced valence attribution for positive pictures. In contrast, anodal tDCS over the vmPFC significantly reduced arousal ratings. Dominance ratings were not affected by the intervention. Our results suggest that the dlPFC is involved in control and regulation of valence of emotional experiences, while the vmPFC might be involved in the extinction of arousal caused by emotional stimuli. Our findings implicate dimension-specific processing of emotions by different prefrontal areas which has implications for disorders characterized by emotional disturbances such as anxiety or mood disorders.
Recursive Deep Models have been used as powerful models to learn \ncompositional representations of text for many natural language processing tasks. \nHowever, they require structured input (i.e. sentiment treebank) to encode sentences \nbased on their tree-based structure to enable them to learn latent semantics \nof words using recursive composition functions. In this paper, we present our \ncontributions and efforts for the Turkish Sentiment Treebank construction. We \nintroduce MS-TR, a Morphologically Enriched Sentiment Treebank, which was \nimplemented for training Recursive Deep Models to address compositional sentiment \nanalysis for Turkish, which is one of the well-known Morphologically Rich \nLanguage (MRL). We propose a semi-supervised automatic annotation, as a distantsupervision \napproach, using morphological features of words to infer the polarity of \nthe inner nodes of MS-TR as positive and negative. The proposed annotation model \nhas four different annotation levels: morph-level, stem-level, token-level, and \nreview-level. Each annotation level’s contribution was tested using three different \ndomain datasets, including product reviews, movie reviews, and the Turkish Natural \nCorpus essays. Comparative results were obtained with the Recursive Neural Tensor Networks (RNTN) model which is operated over MS-TR, and conventional machine learning methods. Experiments proved that RNTN outperformed the baseline methods and achieved much better accuracy results compared to the baseline methods, which cannot accurately capture the aggregated sentiment information.
The paper is an attempt to compare Hyderabad Telugu Treebank (HTTB) and HCU-IIIT-H Telugu Treebank from a statisticalpoint of view. HTTB has 2,715 annotated sentences and HCU-IIIT-H TTB has 3,222 annotated sentences. Both the Treebanks were annotated by following Paninian Grammar Formalism proposed by Bharati, A.; Sharma, D.M.; Husain, S.; Bai, L.; Begam, R. and Sangal, R.(2009).HTTB is an inter-chunk-based treebank data. HCU-IIIT-H TTB is an intra-chunk-based treebankdata. Both the treebanks’ data size is random. Later, the paper discusses the Telugu Treebanks in detail. The paper focuses on statistical frequencies viz. POS, Chunk and Syntactic labels. VM (3807 times) and NN (5486 times) are the frequent POS labels inHTTB and HCU-IIIT-H TTB respectively. NP (7954 and 6223 times) is the frequent phrasal category in both the treebanks. The most frequent k-labels are kartā(k1) (2375-2381 times) and karma(k2) (1408-1437 times) and non-frequent label is karaṇa(k3) (17-39 times) in both the treebanks. The most frequent non-k-labels are verb modifier (vmod) (949 times) and noun modifier (nmod) (1033 times) in both the treebanks. The statistical distribution mentions the coverage of the labels (kāraka, non-kāraka) of both theTelugu treebanks. Later it discusses the comparison of both the treebanks and tries to provide the reasons for the highest and lowest frequencies in both the treebanks. k1 and k2 have 60% of the coverage in karaka labels, vmod, nmod, adv, ccof, pof also has 60% of the coverage in non-karaka labels. This kind of statistical study can help to boost the accuracy of the parser.
Towards explainable affective computing (XAC), researchers have invested considerable effort into post hoc approaches and reverse engineering to seek explanations for deep learning models. However, alternative, intrinsic approaches that aim to build inherently interpretable models by restricting their complexity are yet to be widely explored. In this study, we integrate an explanatory polytomous item response model that provides a well-established psychological interpretation for ordinal scales with deep neural networks to realize high prediction performance and good result interpretability. We conducted an experiment on a growing task (i.e., predicting the idiosyncratic perception of emotional faces of an individual); as expected theoretically, the topmost parameters of our model demonstrated strong correlations with those of the corresponding ordinal item response model: r = 0.928 to 1.00. Our proposed intrinsic approach can used as a complementary framework for post-hoc methods in XAC to coach and support human social interactions.
Arabic dependency parsers have a poor performance compared to parsers of other languages. Recently the impact of annotation at lexical level of dependency treebank on the overall performance of the dependency parses has been extensively investigated. This paper focuses on the impact of coarse-grained and fine-grained dependency relations on the performance of Arabic dependency parsers. Moreover, this paper introduces the annotation rules for I3rab dependency treebank. Experimentally, the obtained results showed that having an appropriate set of dependency relations improves the performance of an Arabic dependency parser up to 27.55%.
Treebanks are valuable linguistic resources that include the syntactic structure of a language sentence in addition to part-of-speech tags and morphological features. They are mainly utilized in modeling statistical parsers. Although the statistical natural language parser has recently become more accurate for languages such as English, those for the Arabic language still have low accuracy. The purpose of this article is to construct a new Arabic dependency treebank based on the traditional Arabic grammatical theory and the characteristics of the Arabic language, to investigate their effects on the accuracy of statistical parsers. The proposed Arabic dependency treebank, called I3rab, contrasts with existing Arabic dependency treebanks in two main concepts. The first concept is the approach of determining the main word of the sentence, and the second concept is the representation of the joined and covert pronouns. To evaluate I3rab, we compared its performance against a subset of Prague Arabic Dependency Treebank that shares a comparable level of details. The conducted experiments show that the percentage improvement reached up to 10.24% in UAS and 18.42% in LAS.
Recent work on multilingual dependency parsing focused on developing highly multilingual parsers that can be applied to a wide range of low-resource languages. In this work, we substantially outperform such "one model to rule them all" approach with a heuristic selection of languages and treebanks on which to train the parser for a specific target language. Our approach, dubbed TOWER, first hierarchically clusters all Universal Dependencies languages based on their mutual syntactic similarity computed from human-coded URIEL vectors. For each low-resource target language, we then climb this language hierarchy starting from the leaf node of that language and heuristically choose the hierarchy level at which to collect training treebanks. This treebank selection heuristic is based on: (i) the aggregate size of all treebanks subsumed by the hierarchy level and (ii) the similarity of the languages in the training sample with the target language. For languages without development treebanks, we additionally use (ii) for model selection (i.e., early stopping) in order to prevent overfitting to development treebanks of closest languages. Our TOWER approach shows substantial gains for low-resource languages over two state-ofthe-art multilingual parsers, with more than 20 LAS point gains for some of those languages. Parsing models and code available at: https: //github.com/codogogo/towerparse.
This paper describes the grammatical patterning of two parts of speech – nouns and adjectives – included in the corpus-driven “Lexical Database of Lithuanian” as a foreign language. The lexical database is a lexicographic application of the Lithuanian Pedagogic Corpus (approx. 620.000 tokens) which was used to develop headword lists and to collect word usage information in the form of corpus patterns. In this project, we adopted a partially automated inductive procedure of Corpus Pattern Analysis for 207 verbs, 386 nouns, 87 adjectives, and 41 adverbs. The detected corpus patterns reflect different meanings of the headword. Each pattern presents information on grammatical, semantic, and lexical levels. Manually selected examples illustrate all pattern components. In this paper, 673 patterns with nouns and 99 patterns with adjectives will be analysed discussing their syntactic behaviour in detail and providing some comments on lexis-grammar interface. The majority of patterns with nouns and adjectives are minimal patterns which include only the closest syntactical partners. This result is influenced by different procedures used to describe patterns with nouns, adjectives, and adverbs and patterns with verbs. Due to rich grammatical information, there are several similar patterns with one main (usually the most frequent) type and its variants. Pattern variants show that the grammatical characteristics of a specific word usage are rather individual.
This article introduces the working methods of the Parsed Historical Corpus of the Welsh Language (PARSHCWL). The corpus is designed to provide researchers with a tool for automatic exhaustive extraction of instances of grammatical structures from Middle and Modern Welsh texts in a way comparable to similar tools that already exist for various European languages. The major features of the corpus are outlined, along with the overall architecture of the workflow needed for a team of researchers to produce it. In this paper, the two first stages of the process, namely pre-processing of texts and automated part-of-speech (POS) tagging are discussed in some detail, focusing in particular on major issues involved in defining word boundaries and in defining a robust and useful tagset.
The aim of the paper is twofold: (1) to automatically predict the ratings assigned by viewers to 14 categories available for TED talks in a multi-label classification task and (2) to determine what types of features drive classification accuracy for each of the categories. The focus is on features of language usage from five groups pertaining to syntactic complexity, lexical richness, register-based n-gram measures, information-theoretic measures and LIWC-style measures. We show that a Recurrent Neural Network classifier trained exclusively on within-text distributions of such features can reach relatively high levels of overall accuracy (69%) across the 14 categories. We find that features from two groups are strong predictors of the affective ratings across all categories and that there are distinct patterns of language usage for each rating category.
Heart Rate Variability (HRV) has been widely studied in laboratory settings due to its clinical implications, primarily as a potential biomarker of emotion regulation (ER). Studies have reported that individuals with higher resting HRV show more distinct startle reflexes to negative stimuli as compared to those with lower HRV. These responses have been associated with better defense system function when managing the context demands. There is, however, a lack of empirical evidence on the association between resting HRV and eyeblinks during laboratory tasks using instructed ER. This study explored the influence of tonic HRV on voluntary cognitive reappraisal through subjective and startle responses measured during an independent ER task. In total, 122 healthy participants completed a task consisting of attempts to upregulate, downregulate, or react naturally to emotions prompted by unpleasant pictures. Tonic HRV was measured for 5 minutes before the experiment began. Current results did not support the idea that self-reported and eyeblink responses were influenced by resting HRV. These findings suggest that, irrespective of resting HRV, individuals may benefit from strategies such as reappraisal that are useful for managing negative emotions. Experimental studies should further explore the role of individual differences when using ER strategies during laboratory tasks.
Jack Rueter, Marília Fernanda Pereira de Freitas, Sidney Da Silva Facundes, Mika Hämäläinen, Niko Partanen. Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas. 2021.
Emotional reactions to movies are typically similar between people. However, depressive symptoms decrease synchrony in brain responses. Less is known about the effect of depressive symptoms on intersubject synchrony in conscious stimulus-related processing. In this study, we presented amusing, sad and fearful movie clips to dysphoric individuals (those with elevated depressive symptoms) and control participants to dynamically rate the clips' valences (positive vs. negative). We analysed both the valence ratings' mean values and intersubject correlation (ISC). We used electrodermal activity (EDA) to complement the measurement in a separate session. There were no group differences in either the EDA or mean valence rating values for each movie type. As expected, the valence ratings' ISC was lower in the dysphoric than the control group, specifically for the sad movie clips. In addition, there was a negative relationship between the valence ratings' ISC and depressive symptoms for sad movie clips in the full sample. The results are discussed in the context of the negative attentional bias in depression. The findings extend previous brain activity results of ISC by showing that depressive symptoms also increase variance in conscious ratings of valence of stimuli in a mood-congruent manner.
We aim to increase user engagement in unfamiliar music. We investigated listening duration for 100 unfamiliar art music items from the Australian Music Centre (AMC) library, presented under four different exposure conditions: a continuous affect response task, text/photographic information, text only, and no information. Participants could skip each item, and provided post-excerpt liking or familiarity ratings. Time-series analysis models of listening duration, liking, and familiarity, showed no increase in successive item liking or familiarity, although user liking and familiarity, positively predicted listening duration. The data confirm that directing listeners’ attention to discerning affect can enhance their engagement with unfamiliar music.
The aim of this article is to generate knowledge about language ideologies in teacher education in Sweden from a critical perspective. In order to achieve an equal education, we argue that it is important that teachers are able to develop an approach and pedagogy that can support all pupils despite their linguistic background to achieve the goals in school. The focus of this article is on language ideologies in teacher education for preschool teachers and how ideological and implementational spaces for language diversity is constructed and negotiated in the education. The empirical material was produced during four years through observations, individual interviews and focus group interviews with educators in the teacher education and a group of ten students in the preschool teacher program, who were admitted to the program based on their migration background. The analysis shows that there is a monolingual standardized norm for Swedish both prevailing in the teacher education and pointing towards their future jobs as preschool teachers. In order to become legitimate members in the group of preschool teacher students and a future community of practice of preschool teachers, the students adjusted to this norm. We identified ideological spaces for multilingualism in the education but the possibilities to implement them were few. Thus, there was a tension between pluralism and diversity on the one side and a strong demand for adjustment to a monolingual standardized language norm for minority students on the other side. As teacher educators we realize the value and necessity of a common language norm, and we are as scholars reproducing such norms of academic language through the writing of this article. At the same time, we argue that it is important to include a multilingual pedagogy in the teacher education that can i) include and support the linguistic repertoires of students in the program and ii) prepare students for their future work in language heterogenous preschools and schools
Recent developments in crowd-sourced data collection and machine intelligence have facilitated data-driven analyses of the affective qualities of urban environments. While past studies have focused on the commonalities of affective experience across multiple subjects, this paper demonstrates an integrated framework for subject-specific affective data collection and predictive modelling. For demonstration, 10 field observers recorded their affective appraisals of various urban environments along the scales of Liveliness, Beauty, Comfort, Safety, Interestingness, Affluence, Stress and Familiarity. Data was collected through a mobile application that also recorded geo-location, date, time of day, a high resolution image of the users field of view, and a short audio clip of ambient sound. Computer vision algorithms were employed for extraction of six key urban features from the images - built score, paved score, auto score, sky score, nature score, and human score. For predictive modelling, K-Nearest Neighbour and Random Forest regression algorithms were trained on the subject-specific datasets of urban features and affective ratings. The algorithms were able to accurately assess the predicted affective qualities of new environments based on the specific individuals affective patterns.
Subject of the work: to find out how S. Maugham was able to use stylistic means in this story. Purpose of the work: to find out what stylistic means were used in this story. Relevance: Stylistics is the science that studies styles of speech and the use of linguistic means in them. This helps to make speech stylistically correct. And the correctness of speech is the basis of speech culture, that is, the ability to assimilate linguistic norms and use the expressive means of language. Stylistics also helps with the formation of skills in the coherent exposition of thoughts in oral and written form. Stylistics introduces the patterns of language use in different spheres of communication, their stylistic originality, and thereby enriches knowledge about the functional aspect of the language. Stylistics as a branch of linguistics is of great importance for the development and theory of language. Conclusion: The purpose of the work has been achieved. We found out what stylistic means were used. Предмет работы: выяснить каким образом С.Моэм смог использовать стилистические средства в этом рассказе. Цель работы: выяснить какие стилистические средства были использованы в этом рассказе. Актуальность: Стилистика - это наука, изучающая стили речи и использование в них языковых средств. Это помогает сделать речь стилистически правильной. А правильность речи - основа речевой культуры, то есть умение усваивать языковые нормы и пользоваться выразительными средствами языка. Стилистика также помогает в формировании навыков связного изложения мыслей в устной и письменной форме. Стилистика знакомит с закономерностями использования языка в разных сферах общения, их стилистической оригинальностью и тем самым обогащает знания о функциональной стороне языка. Стилистика как раздел языкознания имеет большое значение для развития и теории языка. Вывод: Цель работы была достигнута. Мы выяснили какие стилистические средства были использованы. Жұмыс тақырыбы: бұл әңгімеде С.Моэм стилистикалық құралдарды қалай қолдана білгенін білу. Жұмыстың мақсаты: бұл әңгімеде қандай стилистикалық құралдар қолданылғанын білу. Өзектілігі: Стилистика - сөйлеу мәнерлерін және оларда тілдік құралдарды қолдануды зерттейтін ғылым. Бұл сөйлеуді стилистикалық тұрғыдан дұрыс жасауға көмектеседі. Ал сөйлеудің дұрыстығы - сөйлеу мәдениетінің негізі, яғни тілдік нормаларды сіңіріп, тілдің экспрессивті құралдарын қолдана білу. Стилистика сонымен қатар ойды ауызша және жазбаша түрде үйлестіру дағдысын қалыптастыруға көмектеседі. Стилистика қарым-қатынастың әр түрлі салаларында тілдің қолданылу заңдылықтарын, олардың стилистикалық ерекшелігін енгізеді және сол арқылы тілдің функционалдық аспектісі туралы білімді байытады. Стилистика тіл білімінің бір саласы ретінде тілдің дамуы мен теориясы үшін үлкен маңызға ие. Қорытынды: Жұмыстың мақсаты орындалды. Біз қандай стилистикалық құралдар қолданылғанын білдік.
<strong>ACCEPTED ABSTRACT:</strong> <strong>Introduction:</strong> Studies consistently report that patients with schizophrenia exhibit qualitative abnormalities on language production tasks. These abnormalities are possibly associated with the severity of psychotic symptoms. Despite this, some studies have conflictingly suggested that patients with schizophrenia exhibit similar word frequency (WF) effects on lexical tasks compared to healthy subjects. Given that previous studies calculated WFs from language corpora, we aimed to investigate the relationship between WF and psychotic symptoms using a novel, simple method for calculating WF. <strong>Methods:</strong> Thirty-six patients with schizophrenia were included in the study. The severity of positive symptoms was measured using the Scale for the Assessment of Positive Symptoms (SAPS). One semantic and one letter fluency task were administered with the patients instructed to produce as many animal anmes and words beginning with the letter p in 60 s, respectively. Every response in the output was assigned (1) a corpus-based WF, extracted from the German-language lexical database dlexDB, and (2) a within-sample WF. The within-sample WF was calculated as the raw number of participants who produced the word. Spearman’s correlations were computed between the WF variables and symptoms. <strong>Results:</strong> Corpus-based WF exhibited skewed, kurtic, and/or non-normal distribution. Contrastingly, within-sample WF displayed normal, non-skewed, and non-kurtic distribution. There were no significant correlations between corpus-based WF and symptoms on both tasks. Conversely, within-sample WF on semantic fluency was significantly negatively and weakly correlated with the global SAPS score, as well as subscales measuring delusions and bizarre behavior. Further, within-sample WF on letter fluency was significantly positively and weakly correlated with the subscale measuring bizarre behavior of the SAPS scale. <strong>Conclusion:</strong> The differences in the data distribution patterns between corpus-based WF and within-sample WF indicate that different methodological frameworks may have better use of one or the other variable type. Further, significant correlations with positive symptoms were observed only for within-sample WF. It can be concluded that within-sample WF may be more appropriate for analyzing verbal fluency output in psychiatric research compared to corpus-based WF.
The fight against HIV is one of the targets in our century. Thus, among the HIV-infected patients, one of the most dangerous and outstanding with its complications is those with lung pathologies. According clinical staging of the disease, such patients may present Tuberculosis, Pneumocystis jirovecii, Cytomegaloviruses, Candidiasis, Toxoplasmosis etc. The research by scientific research institute of lung disease was carried out among the inpatient individuals in amount of 48.37 (77%) of them were presented with tuberculosis and 11 (23%) with Interstitial Lung Disease (ILD). Studies were presented on HIV-positive patients who were divided by the randomization techniques. Among 37 patients with tuberculosis, 29 (78%) had AFB (acid fast bacillius) with Gexpert, HAIN methods, 6 (22%) were diagnosed by imaging methods (HRCT, chest X-ray) and serum ADA level. According to previous studies, there were no correlations between serum ADA level elevations at HIV-positive patients (p value 0.05). Among 11 patients presented with ILD Pneumocystis jirovecii were detected at 5 (45%), 3 (27.5%) were presented with daily mortality, 3 took a Co-Trimaxozole therapy diagnosed by imaging methods. Clinical effectiveness was approved by the presence of pneumocystis origin. At the second stage of the study was found a correlation between different Cd4 cell count and imaging rating. Thus, among total number of 119 HIV-positive patients, 38 (32%) had infiltration zones, 53 (44%) had a destruction, 20 (17%) dissemination, 8 (7%) mediastinal lymphadenopathy. Statistic results p value 0.000424, thus there is direct correlation.
The Concept Human: New Achievements and Prospects. Iryna Harbera, “Movnoarealʹne pole kontseptu liudyna: Frazeokodovyĭ rivenʹ i linhvokompʹiuterne modeliuvannia” (TOV “Nilan-LTD”, Vinnytsia 2018, ss. 170)The article is a review of an interesting and significant work which summarizes the qualification features of concepts in the modern linguistic paradigm. The specificity of the verbal objectification of concepts is characterized by means of areal phraseology and the concept human in the phraseology of eastern-steppe Ukrainian dialects is structured. A corpus of phraseological units of eastern-steppe Ukrainian dialects with the archisema ‘human’ was formed. The concept human is represented through a system of cultural codes and inter-code transitions. The linguistic database “The Concept Human in the Phraseology of Eastern-Steppe Ukrainian Dialects” was created, based on the ideographic, axiological, structural classification of phraseological units. The ideographic description of the concept human and the analysis of its secondary semiotic system are integrated. Koncepcja człowieka: nowe osiągnięcia i perspektywy. Iryna Harbera, "Movnoarealʹne pole kontseptu liudyna: Frazeokodovyĭ rivenʹ i linhvokompʹiuterne modeliuvannia" (TOV "Nilan-LTD", Vinnytsia 2018, ss. 170)Artykuł jest recenzją interesującej i znaczącej pracy, w której podsumowuje się cechy kwalifikacyjne konceptu we współczesnym paradygmacie językowym. W monografii opisano specyfikę słownego uprzedmiotowienia konceptu za pomocą środków frazeologii gwarowej i uporządkowano koncept człowieka we frazeologii stepowo-wschodnich dialektów ukraińskich. Utworzono korpus frazeologizmów stepowo-wschodnich dialektów ukraińskich z archisemem ‘człowiek’. Koncept człowieka przedstawiono poprzez system kodów kulturowych i przejść pomiędzy kodami. Językowa baza danych „Koncept człowieka we frazeologii stepowo-wschodnich dialektów ukraińskich” wzoruje się na ideograficznej, aksjologicznej i strukturalnej klasyfikacji frazeologizmów. Zintegrowano opis ideograficzny konceptu człowieka i analizę jego wtórnego systemu semiotycznego.
<h3>Introduction</h3><br> <span data-testid="comment-base-item-125611">BOLT Chinese SMS/Chat Parallel Training Data</span> was developed by LDC and consists of approximately 1.8 million tokens of Chinese SMS/Chat data collected for the DARPA BOLT program along with their corresponding English translations <br> The DARPA <a href="https://www.ldc.upenn.edu/collaborations/current-projects/bolt"> BOLT</a> (Broad Operational Language Translation) program developed machine translation and information retrieval for less formal genres, focusing particularly on user-generated content. LDC supported the BOLT program by collecting informal data sources -- discussion forums, text messaging and chat -- in Chinese, Egyptian Arabic and English. The collected data was translated and annotated for various tasks including word alignment, treebanking, propbanking and co-reference. <br> <h3>Data</h3><br> The source date in this release was collected using two methods: new collection via LDC's collection platform, and donation of SMS or chat archives from BOLT collection participants. All data were reviewed manually to exclude any messages/conversations that were not in the target language or that had sensitive content, such as personal identifying information. <br> Data was manually selected for translation. Messages/conversations were arranged in chronological order, segmented into sentence units (all or portions of message threads depending on their length), and assigned to translation vendors. Translators followed LDC's BOLT translation guidelines. <br> Source and translation files are presented in UTF-8 encoded XML format. <br> <h3>Sponsorship</h3><br> This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR0011-11-C-0145. The content does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. <br> <h3>Samples</h3><br> Please view this <a href="desc/addenda/LDC2021T11.cmn.xml">Chinese sample (XML)</a> and <a href="desc/addenda/LDC2021T11.eng.xml">English sample (XML)</a>. <br> <h3>Updates</h3><br> None at this time. </br> Portions © 2021 Trustees of the University of Pennsylvania
We investigate the relative impact of two influential theories of language comprehension, viz., Dependency Locality Theory (Gibson 2000; DLT) and Surprisal Theory (Hale 2001, Levy 2008), on preverbal constituent ordering in Hindi, a predominantly SOV language with flexible word order. Prior work in Hindi has shown that word order scrambling is influenced by information structure constraints in discourse. However, the impact of cognitively grounded factors on Hindi constituent ordering is relatively underexplored. We test the hypothesis that dependency length minimization is a significant predictor of syntactic choice, once information status and surprisal measures (estimated from n-gram i.e., trigram and incremental dependency parsing models) have been added to a machine learning model. Towards this end, we setup a framework to generate meaning-equivalent grammatical variants of Hindi sentences by linearizing preverbal constituents of projective dependency trees in the Hindi-Urdu Treebank (HUTB) corpus of written text. Our results indicate that dependency length displays a weak effect in predicting reference sentences (amidst variants) over and above the aforementioned predictors. Overall, trigram surprisal outperforms dependency length and parser surprisal by a huge margin and our analyses indicate that maximizing lexical predictability is the primary driving force behind preverbal constituent ordering choices in Hindi. The success of trigram surprisal notwithstanding, dependency length minimization predicts non-canonical reference sentences having fronted direct objects over variants containing the canonical word order, cases where surprisal estimates fail due to their bias towards frequent structures and word sequences. Locality effects persist over the Given-New preference of subject-object ordering in Hindi. Accessibility and local statistical biases discussed in the sentence processing literature are plausible explanations for the success of trigram surprisal. Further, we conjecture that the presence of case markers is a strong factor potentially overriding the pressure for dependency length minimization in Hindi. Finally, we discuss the implications of our findings for the information locality hypothesis and theories of language production.
Abstract Engagement in a wide array of mental, social, and physical leisure activities confers several health benefits. Indeed, theories of successful aging argue that an active lifestyle serves as an important criterion for maintaining high levels of psychological, functional, and physical well-being in old age. Findings from parallel studies also show that people who hold positive (self-)views of aging exhibit higher and maintained levels of well-being over time. Yet, whether views of aging enhances the link between activity engagement and well-being - and whether they do so on a daily basis – remains unknown. This study therefore sought to extend prior literature by examining the relationship between activity engagement, subjective age, and affective ratings within-person over several days. Old adults (N = 115; Age: Range = 60 – 90, M = 64.65, SD = 4.86) in the Mindfulness and Anticipatory Coping Every Day (MACED) study completed an 8-day daily diary. Participants reported on their positive and negative affect, the age they subjectively felt compared to their actual age, and the number and types of leisure activities in which they engaged. Results from multilevel analyses indicate that people felt more positive on days when they also engaged in more activities (total across mental, social, physical types) than usual. Moreover, the effect of activity engagement was most pronounced on days when people felt younger than usual. No effects were found for negative affect. Preliminary findings suggest that people benefit psychologically from daily leisure activities and a positive self-view of aging.
Policies regulating immigrant integration constitute a core element of nation-building through the compliance they prescribe with cultural and linguistic norms. The recognition of multiple national belongings in states with national minorities and Indigenous peoples nevertheless challenges majority-centred notions of what integration should entail. Research on connections between integration and recognition, however, has mainly focused on minority substates such as Quebec and Catalonia, where local integration policies align with the respective minority nationalist project, leaving other contexts of recognition largely unexplored. By employing critical and interpretive approaches to the study of politics, this study aims to explore connections, separations, and synergies between policies of national minority recognition and immigrant integration in Europe. Using a combination of document analysis, interviews, and ethnographic observation, it asks how integration policy produces or counters expressions of majority nationhood in states with recognized minorities, how colonial or imperial legacies shape such policies, and what normative tensions can be identified between the promotion of majority and minority identities. Theoretically, it draws on scholarship on liberal multiculturalism, settler colonial studies, and theories on belonging and boundary-making. The four articles of this compilation dissertation combine empirical findings with normative questions. States with recognized minorities in EU27 are shown to reproduce majority nationhood through integration, which clashes with minority protection and with some migrants’ aspirations. In Finland, where the Swedish-speaking minority enjoys equal linguistic recognition with the majority, the minority and migrants are shown to mobilize to ensure the implementation of minority elements in the predominantly majority-centred integration. In Indigenous Swedish Sápmi, state-led integration is found to largely reproduce colonial practices, which are nevertheless also occasionally challenged. In Bulgaria, Turkish-speaking, Muslim minorities are othered in society and marginal within integration, even though post-Ottoman Muslim institutions have come to function as spaces of belonging for recent refugees. Integration policies are shown to misrecognize minorities and thereby fail to represent the actual heterogeneity faced by migrants. Past and present linguistic, religious, racial, and societal contestations are shown to intersect in complex, layered ways that contemporary monolingual, territory-based models of minority recognition and integration fail to capture. The study’s findings have normative implications for research on minority recognition and integration and call for contextually sensitive perspectives to rethink present policies that serve the goals of majority nation-building rather than mirror actual societal belongings.
Resumo: O objetivo deste trabalho é analisar a elaboração de gramáticas para as línguas portuguesa e espanhola, descrevendo aspectos textuais e extratextuais que caracterizam esse processo nas respectivas tradições linguísticas e comparando-as, a fim de encontrar pontos de convergências e divergência. Para tanto, a partir da consulta ao acervo físico e eletrônico de diferentes centros de pesquisa, foi possível a construção de um corpus bibliográfico que permitiu o cumprimento do objetivo do estudo a partir da análise de 172 gramáticas de língua portuguesa e 138 da língua espanhola, distribuídas desde o século XV. Como resultado, foi possível observar que ambas as tradições de codificação trazem compatibilidades históricas que resultaram em um processo de normalização linguística em que muitas características convergem, ao passo que outras divergem. Convergem, por exemplo, na intensificação desse processo a partir do século XIX, marcando um movimento de constante crescimento. Divergem, por outro lado, na quantidade de países engajados no processo e na relação da gramática escolar com o modelo descritivo, por exemplo. Palavras-chave: gramática; norma linguística; historiografia da linguística; língua portuguesa; língua espanhola. Abstract: This paper aims to analyze the elaboration of grammars for the Portuguese and Spanish languages, describing textual and extratextual aspects that characterize this process in the respective linguistic traditions and comparing them in order to find points of convergence and divergence. For that purpose, by consulting the physical and electronic collections of different research centers, it was possible to build a bibliographic corpus which allowed the fulfillment of the study’s objective based on the analysis of 172 Portuguese and 138 Spanish grammars distributed since the 15th century. As a result, it was possible to observe that both codification traditions bring historical compatibilities which resulted in a process of linguistic normalization in which many features converge, while others diverge. They converge, for instance, in the intensification of this process from the 19th century onwards, marking a movement of constant growth. They diverge, on the other hand, in the number of countries engaged in the process and in the relation of school grammar to the descriptive model, for instance. Keywords: grammar; linguistic norm; historiography of linguistics; Portuguese; Spanish.
Literary Works byAkaki Tsereteli are considered as versatile and diverse. In his works he touches upon almost everything by his poetry, prose, journalism or public work. It is obvious that he established "a type of versatile writer who is equally engaged in prose, poetry, journalism, dramaturgy, translations, children's literature and fables”. He was an extremely optimistic person who deeply believed in the future. The following words from one of his works seem amazingly and expressive: “Even if you kill a swallow, Spring will definitely come”. Connection between the old and the new forms, that is clearly shown within this emotionally colored expression, has become the goal of the research. We tried to find an answer to the question- what is the role of using old Georgian forms in Akaki's work?! Given paper analyses the samples such as: 1. Using proper name by its stem form in nominative case; 2. Ending words by - მან [-man] in the ergative form; 3. Full stems of demonstrative pronouns - ‘ამ’ [am], ‘ეგ’ [eg] (=this, that); 4. Using postposition – ‘ზე’ [ze] (=on), along with the forms - ზედ [-zed] and -ზედა [-zeda] (=on, over); 5. instrumental case forms formed by a suffix - ით [-it] (=with) (without postpositions); 6. Postposition and full agreement of attribute and antecedent 7. Characteristics of using inflection as a reflection of Old Georgian (გწყალობდესთ [gtskalobdet]...; გამოვჰკითხავ [gamovhkitkhav]...; ჰსვამ [hvsvam]...; ჰნიშნავს [hnishnavs]...; წარმოსთქვა [tsarmostkva]...; გასტეხე [gastekhe]...); 8. Using conjunction - ვით [vit] (=as/like) for comparison and so on. If we ask questions concerning the function of old Georgian forms in Akaki Tsereteli’s works, it becomes clear that they can be used for: 1. rhythm, emotiveness and expressiveness; 2. Preserving traditional forms, to maintain the connection between old and new Georgian. It should be mentioned that similar forms are equally reflected in Akaki’s prose and poetry which further reinforces the idea in favor of showing the connection between the old and the new and the desire to maintain this connection and always remember where we come from and who we are....This fact does not completely contradict the idea that Akaki is a representative of the generation that courageously rejected the old linguistic norms and contributed to the democratization (rapprochement process with the spoken language) of the literary language.
Cloud-based enterprise search services (e.g., AWS Kendra) have been\nentrancing big data owners by offering convenient and real-time search\nsolutions to them. However, the problem is that individuals and organizations\npossessing confidential big data are hesitant to embrace such services due to\nvalid data privacy concerns. In addition, to offer an intelligent search, these\nservices access the user search history that further jeopardizes his/her\nprivacy. To overcome the privacy problem, the main idea of this research is to\nseparate the intelligence aspect of the search from its pattern matching\naspect. According to this idea, the search intelligence is provided by an\non-premises edge tier and the shared cloud tier only serves as an exhaustive\npattern matching search utility. We propose Smartness At Edge (SAED mechanism\nthat offers intelligence in the form of semantic and personalized search at the\nedge tier while maintaining privacy of the search on the cloud tier. At the\nedge tier, SAED uses a knowledge-based lexical database to expand the query and\ncover its semantics. SAED personalizes the search via an RNN model that can\nlearn the user interest. A word embedding model is used to retrieve documents\nbased on their semantic relevance to the search query. SAED is generic and can\nbe plugged into existing enterprise search systems and enable them to offer\nintelligent and privacy-preserving search without enforcing any change on them.\nEvaluation results on two enterprise search systems under real settings and\nverified by human users demonstrate that SAED can improve the relevancy of the\nretrieved results by on average 24% for plain-text and 75% for encrypted\ngeneric datasets.\n
The presence of a partner can attenuate physiological fear responses, a phenomenon known as social buffering. However, not all individuals are equally sociable. Here we investigated whether social buffering of fear is shaped by sensitivity to social anxiety (social concern) and whether these effects are different in females and males. We collected skin conductance responses (SCRs) and affect ratings of female and male participants when they experienced aversive and neutral sounds alone (alone treatment) or in the presence of an unknown person of the same gender (social treatment). Individual differences in social concern were assessed based on a well-established questionnaire. Our results showed that social concern had a stronger effect on social buffering in females than in males. The lower females scored on social concern, the stronger the SCRs reduction in the social compared to the alone treatment. The effect of social concern on social buffering of fear in females disappeared if participants were paired with a virtual agent instead of a real person. Together, these results showed that social buffering of human fear is shaped by gender and social concern. In females, the presence of virtual agents can buffer fear, irrespective of individual differences in social concern. These findings specify factors that shape the social modulation of human fear, and thus might be relevant for the treatment of anxiety disorders.
espanolEl objetivo de este trabajo es ahondar en el estudio del contacto entre el espanol y el neerlandes, analizando la variacion linguistica que se ha encontrado en un corpus de cartas escritas por cuatro mercaderes neerlandeses entre 1669 y 1677 en Amsterdam y que fueron enviadas a su socio comercial espanol en Bilbao. En particular, la investigacion se centra en la vacilacion en el timbre vocalico y examina como esta variacion se separa y diferencia de la variacion propia del espanol peninsular del siglo XVII. Se concluye que los autores extienden la variacion a contextos que no formaban parte de la norma del momento, probablemente debido a la variabilidad que se asocia con el sistema de la interlengua. Sin embargo, tampoco se descarta la acomodacion de los autores a la norma variable del siglo XVII para los casos en que la vacilacion vocalica se adecua a la que existia entonces. EnglishThe objective of this research is to broaden in the study of the linguistic contact between Spanish language and Dutch. I will analyze the linguistic variation found in a corpus of letters written by four Dutch merchants in Amsterdam between 1669-1677 and sent to their Spanish counterpart in Bilbao. Specifically, I will examine the vowel variation found in the corpus and how this variation is different from the one characteristic of the Peninsular Spanish of the moment. I conclude that authors extend the variation to contexts that were not part of the linguistic norm of 17th-century Spanish. This is probably due to the variability of the interlanguage sys-tem. However, I do not disclaim that authors accommodate to the variable norm of the Spanish of that time in the cases that they behave accordingly.
Summary In this article, we provide preliminary evidence for the ‘hypersensitivity hypothesis’, according to which Emotional Intelligence (EI) functions as a magnifier of emotional experience, enhancing the effect of emotion and emotion information on thinking and social perception. Measuring ability EI, and in particular Emotion Understanding, we describe an experiment designed to determine whether, relative to those low in EI, individuals high in EI were more affected by the valence of a scenario describing a target when making an affective social judgment. Employing a sample of individuals from the general population, high EI participants were found to provide more extreme (positive or negative) impressions of the target as a function of the scenario valence: positive information about the target increased high EI participants’ positive impressions more than it increased low EI participants’ impressions, and negative information increased their negative impressions more. In addition, EI affected the amount of recalled information and this led high EI individuals to intensify their affective ratings of the target. These initial results show that individuals high on EI may be particularly sensitive to emotions and emotion information, and they suggest that this hypersensitivity might account for both the beneficial and detrimental effects of EI documented in the literature. Implications are discussed.
In this paper, we propose a method for learning representations in the space of Gaussian-like distribution defined on a novel geometrical space called Kinematic space. The utility of non-Euclidean geometry for deep representation learning has recently been in vogue, specifically models of hyperbolic geometry such as Poincaré and Lorentz models have proven useful for learning hierarchical representations. Going beyond manifolds with constant curvature, albeit has better representation capacity might lead to unhanding of computationally tractable tools like Riemannian optimization methods. Here, we explore a pseudo-Riemannian auxiliary Lorentzian space called Kinematic space and provide a principled approach for constructing a Gaussian-like distribution, which is compatible with gradient-based learning methods, to formulate a probabilistic word embedding framework. Contrary to, mapping lexically distributed representations to a single point vector in Euclidean space, we advocate for mapping entities to density-based representations, as it provides explicit control over the uncertainty in representations. We test our framework by embedding WordNet-Noun hierarchy, a large lexical database, our experiments report strong consistent improvements in Mean Rank and Mean Average Precision (MAP) values compared to probabilistic word embedding frameworks defined on Euclidean and hyperbolic spaces. We show an average improvement of 72.68% in MAP and 82.60% in Rank compared to the hyperbolic version. Our work serves as evidence for the utility of novel geometrical spaces for learning hierarchical representations.
固有表現認識は，科学技術論文などのテキストから分野特有の用語を機械的に抽出するタスクである．固有表現認識の従来研究は連続した範囲から成る固有表現のみを解析対象としているが，並列する固有表現の一部が省略された複合的表現が含まれており，これらの固有表現に対して個々の固有表現を抽出することが困難である．本研究では，近年の自然言語処理タスクで広く使用されている学習済み言語モデルを用いて，並列構造の教師データを用いずに並列する句の範囲を同定し，複合化された固有表現を正規化する手法を提案する．GENIA Treebank と GENIA term annotation を用いた評価実験では，教師情報を使用した先行研究と近い解析性能を示し，提案手法によって固有表現認識の精度が向上することを確認した．
Cameroon, a central African country, is one of the most linguistically diverse countries in Africa with about 280 living languages (Ethnologue 2020), for an estimated population of 26,727,521 people (Worldometer, 2020). Cameroon is second only to Papua New Guinea in terms of its multiplicity of languages for a relatively small population. Contrary to popular opinion, multilingualism exists even in rural communities; in fact, it is even more intense. In Lower Fungom, an incredibly linguistically diverse rural community in the Northwest region of Cameroon, high rates of individual multilingualism are the norm; it is common to find individuals who use more than seven distinct native languages to navigate through their daily lives. However, this multilingualism is usually neglected as a resource by foreign experts in the transmission of knowledge in linguistically diverse communities such as Lower Fungom. In their attempt to transmit knowledge in almost all ramifications including in the global pursuit of sustainable development, experts foreign to the target community typically focus only on the ‘understanding’ of their message, meanwhile ‘understanding’ could be totally inconsequential as far as the acceptance of a people is concerned. Sustainable development with trends away from the (socio-cultural and linguistic) norms of a community would be a complete farce. This paper aims at highlighting two key features indispensable for development to be extended to rural communities in Cameroon and for it to be sustainable. These aspects are the active collaboration with community members to obtain culturally appropriate interpretations and the use of all the languages existing in the community in transmitting knowledge. Data for this paper comprises recorded natural speeches, interviews, and observation notes due to prolonged stays in the area and resultant informal discussions with its indigenes. This study will not only add to the handful of studies on rural multilingualism. It will not also only promote multilingualism that has become an endangered practice, but it will also be a crucial addition to efforts of sustainable development in Cameroon.
Writers from a number of theoretical backgrounds have asserted that agreement in the emotional messages conveyed by various verbal and nonverbal communication channels is related to the communicator's psychological health. If this conjecture is accurate, then congruence among communication channels could be used as a behaviorally based assessment tool. However, empirical research to test this theoretical and clinical assumption is relatively lacking. The present study was designed to test the hypothesis that individuals who display congruence (agreement) between verbal (language), verbal/vocal (language plus paralinguistic cues, or speech) and nonverbal (facial) channels of communication will show a greater degree of mental health than will individuals who display incongruence. "Degree of mental health" was operationally defined as an individual's scores on the Personal Orientation Inventory (POI). Fifty-six subjects were administrated the POI and were interviewed on videotape. Three pairs of judges rated the videotapes for the affects communicated in the video channel (picture only), the audio channel (sound only), and the transcript channel (the subject's words transcribed onto paper). Comparisons of affect ratings across channels yielded difference scores, resulting in measures of various types of congruence. Analyses of variance were carried out with difference scores as independent variables and and overall POI score as the dependent variable. No significant results were obtained. Multivariate analyses of the POI subscales were also performed, again with nonsignificant findings. Alternative explanations of the congruence phenomenon and methodological limitations are presented. Implications for the clinical utility of congruence and for future research are discussed.
Politicians are skilled language users who deploy words strategically and pay close attention to the emotions that those words evoke. We examined the emotional characteristics of over 92 million words spoken by Canadian Members of Parliament between 2006 and 2021. The analysis brought together the Warriner, Kuperman, and Brysbaert (Behav. Res., 2013, 45, 1191–1207) database of valence (positivity) ratings for English and the Canadian Hansard, which contains a transcription of parliamentary speech. Results revealed that the positivity of words used by politicians in parliament was significantly related to both political and social variables. Politicians increased the positivity of their language after the onset of the COVID-19 crisis. Within the time of the crisis, word positivity was linked statistically to month-by-month case counts, indicating a very fine-grained sensitivity to social realities. Our analysis also revealed a fine-grained sensitivity of word valence to political realities. As expected, parties in power used more positive language than those in opposition. In addition, our analysis revealed that individual parties have characteristic levels of word positivity and that those levels change in accordance with political changes as specific as whether or not the party in power holds a majority of seats in parliament. These findings suggest that the emotional properties of words used by Members of Parliament are reliably indexed to sociopolitical dynamics. The findings also suggest that the methodology of linking individual word ratings to Hansard Documents (which are used to document Parliamentary activities in over 25 countries) can provide a key tool for the understanding of specific crises such as the COVID-19 global pandemic as well as more general social and political trends across countries and languages.
The linguistic worldview is a reflection of the national cognitive worldview. ‘Worldview’ is often defined as a way of perceiving the surrounding reality, yet the way people perceive their personal inner world also reflects their national self-identification. It is difficult to compare how people of different nations experience emotions and perceive such experiences because these processes are not available for direct observation and objective assessment. The most complete representation of the way a person experiences a particular emotion can be found in fiction. Contrastive analysis of how this process is reflected in different languages can be based on a comparison of a literary text with its translation into another language, since, in this case, both texts present the same character in the same situations that cause certain emotions. To exclude the influence of the translator’s personality, in our analysis we have used three different translations of selected passages from Dostoevsky’s The Idiot. A quantitative analysis of the means employed by the translators shows that representation of emotions in English does indeed reflect the way of perceiving the world that is typical of the national linguistic worldview as a whole. In all the three English texts, state predicates prevail over ac-tion predicates, and predicatively used adjectival words prevail over those used attributively. It means that emotional states are mostly perceived by English speakers as something that, while not permanent or inherent to a person, is, at the same time, static: less a process than a result of that process. In contrast, native speakers of Russian perceive emotional states as actions, and the Russian text reveals no inclination toward perceiving emotional states as personal characteristics, whether temporary or permanent. All these regularities are statistical and not absolute, which means that they reflect usage and not the linguistic norm, and, thus, the change of predicates in translation should be regarded as a way of cognitive adaptation rather than a structural transformation.
The main motivation behind this exam document is to look at the extent to which EWOM among customers can affect the brand image and the intent of buying the consumer in the clothing industry. A key condition display process is linked to the E-WOM impacts survey on brand image and buyer's purchase target. The exploration program was tested using an example of 385 respondents who included information within online purchasing groups and examined buyers of Pakistan's textile industry at the time of the investigation. The document recalls the methodologies to help a brand profitably through client-based social networking on the web, as well as typical suggestions for delegated websites and dialogues to enhance this note on a major path with people in their online dating. This explorative document extends the winning image rating to another set, in particular e-WOM. This document provides profitable knowledge on e-WOM estimation, brand image and purchasing expectations of the purchaser in the clothing industry and provides a facility for future search for tagging items.
In this article, we present our methodologies for SemEval-2021 Task-4: Reading Comprehension of Abstract Meaning. Given a fill-inthe-blank-type question and a corresponding context, the task is to predict the most suitable word from a list of 5 options. There are three sub-tasks within this task: Imperceptibility (subtask-I), Non-Specificity (subtask-II), and Intersection (subtask-III). We use encoders of transformers-based models pre-trained on the masked language modelling (MLM) task to build our Fill-in-the-blank (FitB) models. Moreover, to model imperceptibility, we define certain linguistic features, and to model non-specificity, we leverage information from hypernyms and hyponyms provided by a lexical database. Specifically, for non-specificity, we try out augmentation techniques, and other statistical techniques. We also propose variants, namely Chunk Voting and Max Context, to take care of input length restrictions for BERT, etc. Additionally, we perform a thorough ablation study, and use Integrated Gradients to explain our predictions on a few samples. Our best submissions achieve accuracies of 75.31% and 77.84%, on the test sets for subtask-I and subtask-II, respectively. For subtask-III, we achieve accuracies of 65.64% and 62.27%. The code is available here.
Abstract Drug addiction is characterized by impaired Response Inhibition and Salience Attribution (iRISA), where the salience of drug cues is postulated to overpower that of other reinforcers with a concomitant decrease in self-control. However, the neural underpinnings of the interaction between the salience of drug cues and inhibitory control in drug addiction remain unclear. We developed a novel stop-signal fMRI task where the stop-signal reaction time (SSRT—a classical inhibitory control measure) was tested under different salience conditions (modulated by drug, food, threat or neutral words) in individuals with cocaine use disorder (CUD; n=26) vs. demographically matched healthy control participants (HC; n=26). Despite similarities in drug cue-related SSRT and valence and arousal word ratings between groups, dorsolateral prefrontal cortex (dlPFC) activity was diminished during the successful inhibition of drug versus food cues in CUD, and was correlated with lower frequency of recent use, lower craving, and longer abstinence (Z>3.1, p <.05 corrected). Results suggest altered involvement of cognitive control regions (e.g., dlPFC) during inhibitory control under a drug context, relative to an alternative reinforcer, in CUD. Supporting the iRISA model, these results elucidate the direct impact of drug-related cue-reactivity on the neural signature of inhibitory control in drug addiction.
Abstract Background: Studies on food cue reactivity have documented that altered responses to high-calorie food are associated with bulimic symptomatology, however, alterations in sexual motivations and behaviors are also associated clinical features for this population, which justify their inclusion as a research target. Here, we study responses to erotic cues – alongside neutral and aversive cues – to gain an understanding of specificity to food vs. a generalized sensitivity to primary reinforcers. Methods: We recorded peripheral psychophysiological indices –the startle reflex, zygomaticus, and corrugator responses– and self-reported emotional responses (valence, arousal, and dominance) in 75 women that were presented with the Spanish version of the Bulimia Test-Revised (BULIT-R). Multiple regression analysis tested whether BULIT-R symptoms were predicted by self-reported and psychophysiological responses to food vs. neutral and erotic vs. neutral images. Results: The results showed that individuals with higher bulimic symptoms were characterized by potentiated eye blink startle response during binge food (vs. neutral images) and more positive valence ratings during erotic (vs. neutral) cues. Conclusions: The results highlight the negative emotional reactivity of individuals with elevated bulimic symptoms toward food cues, which could be related to the risk of progression to full bulimia nervosa and thereby addressed in prevention efforts. Results also point to the potential role of reactivity to erotic content, at least on a subjective level. Theoretical models of eating disorders should widen their conceptual scope to consider reactivity to a broader spectrum of primary reinforcers, which would have implications for cue exposure-based treatments.
The purpose of this study is to examine the orthographic and phonological characteristics of the Yeongsan Sillok(the biography of Yeongsan), published in Jeollabuk-do in the early 20th century. The author of this book is considered to be Jang Bong-seon, an educator from Jeongeup city in Jeollabuk-do. Accordingly, it is expected that this book contains the orthographic characteristics and attitudes toward the language of young intellectuals in Jeollabuk-do in the early 20th century. In Chapter 3, we looked at the orthographic characteristics of this book. The writing characteristics of this book largely follow the characteristics of the 19th century Jeollabuk-do dialect based on the tradition of modern Korean. However, a transitional characteristic of the language transforming into present-day Korean was also present. Although only a few examples have been confirmed, the writing of double consonant letters for tense consonant are gradually similar to the notation method of modern Korean. This can be understood as a dissolution process. At the same time, with the exception of some circumstances of verbs, the tendency to split consonants is widely confirmed, and the modern Korean notation for the /ㄹㄹ/ chain (ㄹㄴ, ​​ㄹㅇ) is gradually changing to ㄹㄹ. Above all, the fact that the notation of ․ or diphthong after sibilants no longer appears in this book is a characteristic feature that differs from data from the Jeollabuk-do region of the same period. This writing trend seems to be related to a set of linguistic norms compiled in the first half of the 20th century. Recalling that the author of this book established a private school in the 1920s and 1930s and devoted himself to educational activities, this assumption is somewhat probable. In Chapter 4, we looked at the phonological characteristics of the Yeongsan Sillok(the biography of Yeongsan). Front-vowelization was very active inside the morpheme, but at the morpheme boundary, it appeared only in the environment behind c. The simple vowelization of jə>e is confirmed throughout the interior and boundary of the morpheme, and it must have been a productive phonological phenomenon in the Jeollabuk-do dialect in the early 20th century, as hypercorrection types also appeared. Regarding the alternation of the ending ‘-a/ə’, when the stem vowel is ‘ø’, there is a high tendency to combine these to ‘-ə’. This is different from the 19th century and modern Jeollabuk-do dialects. In the case of umlauts, only very limited examples were shown. And although t-palatalization is quite actively realized, only a few examples of k-palatalization were shown. Through this realization of phonological phenomena, we were able to confirm whether the young intellectuals in the Jeollabuk-do region in the early 20th century had linguistic attitudes toward the Jeollabuk-do dialect. In this book, the typical phonological phenomenon of the Jeollabuk-do dialect was confirmed only to a very limited extent due to its negative evaluation by the author.
Time perception is not veridical, but, rather, it is susceptible to environmental context, like the intrinsic dynamics of moving stimuli. The direction of motion has been reported to affect time perception such that the movement of objects toward an observer is perceived as longer in duration than that of objects away from the observer. This looming-motion-induced time dilation has been explained in terms of an arousal-based or an attentional mechanism (or a combination of both). The current study was interested in which of these two explanations represents a more viable mechanism. With this aim, we investigated how the looming/receding temporal asymmetry is modulated by the emotional contents of stimuli. In two experiments, participants were shown face images expressing three emotions (angry, happy, and neutral) for one of seven target durations (400-1000ms) and performed a temporal bisection task by judging each presentation duration as “short” or “long”. In Experiment 1, the face images were shown in a constant-sized, stationary position. In Experiment 2, the images were expanding (looming) or contracting (receding) in size. In Experiment 1, we found no influence of facial emotion in perceived duration. In Experiment 2, however, looming stimuli were perceived as longer in duration than receding ones, replicating previous findings of the looming-induced time dilation using naturalistic human-face stimuli. More importantly, in Experiment 2 we found an interaction effect between arousal rating of faces and motion direction: The looming/receding asymmetry was pronounced when the arousal of the presented images was rated low, but this asymmetry diminished when arousal was high. These results suggest that (1) affective characteristics of looming stimuli can modulate temporal processing and more specifically, (2) the looming/receding asymmetry is reduced when arousing facial expressions enhance attentional engagement to receding stimuli, supporting the attentional mechanism of the looming-induced time dilation.
Recent impressive improvements in NLP, largely based on the success of contextual neural language models, have been mostly demonstrated on at most a couple dozen high- resource languages. Building language mod- els and, more generally, NLP systems for non- standardized and low-resource languages remains a challenging task. In this work, we fo- cus on North-African colloquial dialectal Arabic written using an extension of the Latin script, called NArabizi, found mostly on social media and messaging communication. In this low-resource scenario with data display- ing a high level of variability, we compare the downstream performance of a character-based language model on part-of-speech tagging and dependency parsing to that of monolingual and multilingual models. We show that a character-based model trained on only 99k sentences of NArabizi and fined-tuned on a small treebank of this language leads to performance close to those obtained with the same architecture pre- trained on large multilingual and monolingual models. Confirming these results a on much larger data set of noisy French user-generated content, we argue that such character-based language models can be an asset for NLP in low-resource and high language variability settings.
Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context. Contextual word embedding models have achieved state-of-the-art results in the lexical substitution task by relying on contextual information extracted from the replaced word within the sentence. However, such models do not take into account structured knowledge that exists in external lexical databases. We introduce LexSubCon, an end-to-end lexical substitution framework based on contextual embedding models that can identify highly accurate substitute candidates. This is achieved by combining contextual information with knowledge from structured lexical resources. Our approach involves: (i) introducing a novel mix-up embedding strategy in the creation of the input embedding of the target word through linearly interpolating the pair of the target input embedding and the average embedding of its probable synonyms; (ii) considering the similarity of the sentence-definition embeddings of the target word and its proposed candidates; and, (iii) calculating the effect of each substitution in the semantics of the sentence through a fine-tuned sentence similarity model. Our experiments show that LexSubCon outperforms previous state-of-the-art methods on LS07 and CoInCo benchmark datasets that are widely used for lexical substitution tasks.
Cloud-based enterprise search services (e.g., AWS Kendra) have been entrancing big data owners by offering convenient and real-time search solutions to them. However, the problem is that individuals and organizations possessing confidential big data are hesitant to embrace such services due to valid data privacy concerns. In addition, to offer an intelligent search, these services access the user's search history that further jeopardizes his/her privacy. To overcome the privacy problem, the main idea of this research is to separate the intelligence aspect of the search from its pattern matching aspect. According to this idea, the search intelligence is provided by an on-premises edge tier and the shared cloud tier only serves as an exhaustive pattern matching search utility. We propose Smartness at Edge (SAED mechanism that offers intelligence in the form of semantic and personalized search at the edge tier while maintaining privacy of the search on the cloud tier. At the edge tier, SAED uses a knowledge-based lexical database to expand the query and cover its semantics. SAED personalizes the search via an RNN model that can learn the user's interest. A word embedding model is used to retrieve documents based on their semantic relevance to the search query. SAED is generic and can be plugged into existing enterprise search systems and enable them to offer intelligent and privacy-preserving search without enforcing any change on them. Evaluation results on two enterprise search systems under real settings and verified by human users demonstrate that SAED can improve the relevancy of the retrieved results by on average ≈24% for plain-text and ≈75% for encrypted generic datasets.
In this paper, we address the representation of coordinate constructions in Enhanced Universal Dependencies (UD), where relevant dependency links are propagated from conjunction heads to other conjuncts. English treebanks for enhanced UD have been created from gold basic dependencies using a heuristic rule-based converter, which propagates only core arguments. With the aim of determining which set of links should be propagated from a semantic perspective, we create a large-scale dataset of manually edited syntax graphs. We identify several systematic errors in the original data, and propose to also propagate adjuncts. We observe high inter-annotator agreement for this semantic annotation task. Using our new manually verified dataset, we perform the first principled comparison of rule-based and (partially novel) machine-learning based methods for conjunction propagation for English. We show that learning propagation rules is more effective than hand-designing heuristic rules. When using automatic parses, our neural graph-parser based edge predictor outperforms the currently predominant pipelines using a basic-layer tree parser plus converters.
The aim of this paper is to offer an insight into the semantic roles of adverbials. The approach is mainly construed around the theory of adverb semantics propounded by Quirk, Greenbaum, Leech, and Svartvik (1985) – grammatical functions and the realisation of semantic roles. The theoretical approach is complemented by a practical analysis of adverbial phrases occurring in social interactions (as well as script-based stage directions) from the TV series “Friends”. The main method used is corpus analysis; in addition, a semi-automated identification of adverbs was performed using both quantitative and qualitative analyses. The tools used were ConcApp software, as well as electronic dictionaries and lexical databases. A quantitative and qualitative analysis of -ly adverbials in the script was carried out to establish certain patterns of adverb occurrence in social interaction. The results reveal a large proportion of subjuncts, in particular emphasisers, intensifier subjuncts and downtoners (approximator) (in Greenbaum et al.’s taxonomy), or, in other taxonomies, speaker-oriented (Jackendoff 1972) / sentence adverbs (Swan 1988) / stance adverbs – attitude and epistemic (Biber et al. 1999). A second important finding is that the –ly adverbs used in this sitcom display high polysemy, including some novel semantic uses peculiar to present-day US English.
Labeling data can be an expensive task as it is usually performed manually by\ndomain experts. This is cumbersome for deep learning, as it is dependent on\nlarge labeled datasets. Active learning (AL) is a paradigm that aims to reduce\nlabeling effort by only using the data which the used model deems most\ninformative. Little research has been done on AL in a text classification\nsetting and next to none has involved the more recent, state-of-the-art Natural\nLanguage Processing (NLP) models. Here, we present an empirical study that\ncompares different uncertainty-based algorithms with BERT$_{base}$ as the used\nclassifier. We evaluate the algorithms on two NLP classification datasets:\nStanford Sentiment Treebank and KvK-Frontpages. Additionally, we explore\nheuristics that aim to solve presupposed problems of uncertainty-based AL;\nnamely, that it is unscalable and that it is prone to selecting outliers.\nFurthermore, we explore the influence of the query-pool size on the performance\nof AL. Whereas it was found that the proposed heuristics for AL did not improve\nperformance of AL; our results show that using uncertainty-based AL with\nBERT$_{base}$ outperforms random sampling of data. This difference in\nperformance can decrease as the query-pool size gets larger.\n
This research is aimed to describe the language attitude of the people of Mandar, a migrant community in Desa Baharu Utara, Kotabaru Regency. The community group chosen as the object of the research is the young generation (Generasi Muda or GM) of Mandar. Therefore, the respondents are 40 people in various age groups consisting of children, adolescents, and adults with an age range of 6-45 years. Data collection of language attitudes was carried out using a questionnaire which was supported by field observations at the research location. It was found on the research location that GM is more proficient in Banjarese Language (Bahasa Banjar or BB) than Mandar (Bahasa Mandar or BM). This is based on the reality that BB is a local language with high prestige. On the other hand, BB has a strategic role as a lingua franca, which is the language of communication between ethnic groups in the Kotabaru area. Meanwhile, BM, which is the language of minority migrants from West Sulawesi, tends to be pushed by BB's domination because it has lost its prestige. As a result, BM experiences a shift from time to time which is feared to lead to extinction. The shift occurs at various linguistic levels, both phonemes, morphemes, and lexicon. The results of field observations indicate that the older generation (Generasi Tua or GT) has a more positive attitude towards BM than the GM of Mandar. The language attitudes include 1) pride in using BM, 2) loyalty to BM related to the level of frequency of using BM, and 3) awareness of BM norms related to linguistic norms and social norms related to BM usage situations and domains of use BM.