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16504 papers
Language users and learners are sensitive to distributional information in their environment, which enables them to extract regularities that occur in the language input that they are exposed to. This process is referred to as statistical learning. While the statistical learning phonotactic literature thoroughly investigates the learning of overall phonotactics in specific languages, little is known about cases where different phonological systems coexist within a single language. The Japanese lexicon is generally classified into four lexical strata according to the etymological status of each word (Itô & Mester, 1995, 1999, 2001). Although each stratum includes the internal phonological similarity in the Japanese language as a whole, there are also distinctive phonological properties. A recent study suggests that language users should be able to learn phonotactics of each sublexicon based on the same kind of statistical probabilities that computers analyse from language users’ accumulated lexicons (Morita, 2018). This thesis examines whether second-language (L2) learners can learn the loanword phonotactics/phonology of Japanese through experience of using and/or passive exposure to Japanese lexical stratification. Using two loanword phonological regularities (categorical and gradient rules) as a case study, two fully-crossed perceptual experiments involving English- speaking learners of Japanese, native speakers of Japanese, and English-speaking monolinguals are presented. The first experiment explores listeners’ phonotactic/phonological knowledge of nativised loanwords in Japanese using a well-formedness task which shows the adaptation of English final consonants in monosyllabic words. Listeners judge whether the pronunciation they hear is how the word would be pronounced if it was a Japanese word, rating how confident they are on a scale of 1-5. This study shows that L2 learners learn categorical rules, but not gradient patterns. This study also confirms that loanword phonotactics and overall phonotactics make separate contributions to perceived well-formedness. L2 learners access and make use of the sublexicon-specific probabilities of Japanese during the task. The second perceptual experiment is designed to support the findings in the first experiment, by testing for discrimination of non-native consonantal contrasts. Even under high memory demand, L2 learners show the ability to discriminate non-native consonantal contrasts (i.e., CVCV/CVCCV) effectively enough to support findings in the first experiment. These results suggest that L2 learners can implicitly detect the statistical structure of a language’s sublexicon phonology over the course of acquiring a natural language. However, while native speakers of Japanese learn a gradient rule, L2 learners of Japanese do not. A potential explanation for the differences in gradient rule learning is that the vocabulary size of the target language might play a crucial role. This remains an open question. In addition, the present work provides a basis for future investigation into whether L2 learners of Japanese, whose native language is other than English, are able to learn Japanese loanword phonotactics/phonology. L1 English-L2 Japanese speakers might gain advantage in perceiving the English input which inevitably overlaps with the phonological form of the host language.
This research will focus on the use of Javanese communication related to the form of family greetings that can be seen from the completeness of its elements. Javanese communication forms of family greetings are divided into three, namely: complete greeting forms, incomplete greeting forms, and a combination of complete greeting forms and incomplete greeting forms. Whereas based on the meanings and meanings of language communication, the form of family greetings can be in the form of self-names, kinship terms, paraban, national titles, adjective transpositions, and beatings. Factors that influence Javanese communication in the form of family greetings are the position of parents towards their children viewed from various aspects of course higher, but related to the use of the form of greeting it turns out that its use often shows a respectful form of greeting. This can be related to the role of the first person as a parent whose obligation is to educate and direct their children to be good children, who have good manners and can respect others and also their own parents. Other things that affect the form of family greetings are the first person, second person, third person, the meaning of the speaker, the color of the emotion, the tone of the speech, the subject, speech sequence, form of discourse, speech facilities, speech scenes, speech environment, and linguistic norms.
 
 Abstrak
 Penelitian ini akan difokuskan pada penggunaan komunikasi bahasa Jawa yang berkaitan dengan bentuk sapaan keluarga yangdapat dilihat dari kelengkapan unsur-unsurnya. Komunikasi bahasa Jawa bentuk sapaan keluarga dibedakan menjadi tiga, yaitu: bentuk sapaan lengkap, bentuk sapaan tak lengkap, dan gabungan bentuk sapaan lengkap dan bentuk sapaan tak lengkap. Sedangkan berdasarkan makna dan artinya komunikasi Bahasa bentuk sapaan keluarga dapat berupa nama diri, istilah kekerabatan, paraban, gelar kebangsawaan, transposisi ajektif dan poyokan. Faktoryang mempengaruhi komunikasi Bahasa Jawa dalam bentuk sapaan keluargaadalah posisi orang tua terhadap anak-anaknya dilihat dari berbagai segi tentunya lebih tinggi, namun berkaitan dengan pemakaian bentuk sapaan ternyata sering sekali penggunaannya justru menunjukan bentuk sapaan yang hormat. Hal ini dapat dikaitkan dengan peran orang pertama sebagai orang tua yang salah satu kewajibannya adalah mendidik dan mengarahkan anak-anaknya agar menjadi anak yang baik, yang memiliki sopan santun dan dapat menghormati orang lain dan juga orang tuanya sendiri. Hal lain yang mempengaruhi bentuk sapaan keluarga adalah orang pertama, orang kedua, orang ketiga, maksud penutur, warna emosi, nada suasana bicara, pokok pembicaraan, urutan bicara, bentuk wacana, sarana tutur, adegan tutur, lingkungan tutur, dan norma kebahasaan.
 Kata kunci: bentuk sapaan, komunikasi Bahasa Jawa
Introduction. High-quality language education in technical universities requires its interdisciplinary relation to the content of highly specialised subjects corresponding to the training programmes aimed at instructing the future specialists. Educational materials in a foreign language are highly productive if they emphasise the terminology and professional vocabulary authentic to the current state of the scientific field. The aim of the study presented in the article was to assess the validity of the lexical material delivered in the course “English for Business Communication”, to determine the selection criteria for this vocabulary as well as the methods for its assimilation and practical application. Methodology and research methods. The applied corpus software enabled to obtain quantitative indicators of the distribution of foreign-language business vocabulary in the given training course. The lexical material being currently offered to students and the professional thesaurus identified via linguistic databases was compared with the use of comparative analysis and synthesis. Results and scientific novelty. The lexical units (terms, set expressions), which are the most active in the business sphere, were identified on the basis of its frequency. The authors established the correlation between them and educational vocabulary, both from the perspective of its integration into the course without block concentration throughout the course of university training, and from the perspective of the variety of methods used to practice this vocabulary. It is concluded that the applied educational material needs to be substantially adjusted. The vocabulary does not completely reflect the realities of the business communication sphere and the distribution of active vocational vocabulary regulated by methodological guidelines does not entirely contribute to its strong assimilation. According to the authors, the necessary changes to the approaches and methods for selecting and compiling lexical material and to the methodology for designing a foreign language course should be made on the basis of integrating pedagogical and linguistic knowledge, in particular, the methodology of teaching foreign languages and the corpus linguistics. Practical significance. The ways of integrating corpus programs in the process of developing the content of language disciplines, which are part of the main educational program of technical universities, are demonstrated as one of the methods to increase the effectiveness of teaching foreign languages to students of non-linguistic specialties.
The aim of the study is to design an electronic terminographic product in the form of a terminological data bank (TDB) “Classification Parameters of Phraseological Units” with consistent implementation of infological and datological stages. The object in the study is the TDB, the focus is terms for designation of types of phraseological units (PU), which actively function in Ukrainian and Russian linguistics in the 2nd half of the 20th century to 2020. The methodology of the study is based on theoretical foundations of phraseology, terminology, applied linguistics, and computer terminography (linguistic database/data bank technology). The general and specific scientific methods are used: analysis and synthesis, ascent from the abstract to the concrete, classification, definitional analysis, identification, linguistic observation and description, selection, elements of a statistical method. The infological stage is related to the solution of a set of information problems: 1) definition of the principle of regularization of terms of phraseology, 2) selection of terms with the archeseme “phraseoclassification type” from scientific literature in order to form an alphabetical register, 3) establishment of the composition of the dedicated terminology subsystems, 4) description of terminology subsystems, 5) creation of an electronic catalog of terms for further computer processing of collected information. The main principle of systematization is the thesaurus: “terminological system – terminological microsystem – terminological subsystem – term”. The next step is to develop a datalogical scheme, which is a system of tables whose fields display information about the described terms in the form of data, for example: number, term, terminology subsystem, scientific source, definition, illustration, paradigm relations of the term. The TDB modeled in the work is a special terminology dictionary (monolingual (Ukrainian) in terms of the number of languages involved), which has 113 terms to denote types of PUs and covers 17 classifications of PUs. The TDB “Classification Parameters of Phraseological Units” is positioned as an electronic terminographic product created for storage of information, optimization and intensification of system research on fundamental issues of phraseology and phraseography, works devoted to problems of comprehensive analysis and parameterization of PUs. The prospect of the study is to create a Russian-language version of this terminographic product.
Existing approaches for graph-based deep dependency parsing that force connexity in predicted graphs do no cover the structures observed in French treebanks. We propose a novel algorithm that covers the full set of possible structures. We evaluate our approach on the French corpora FTB and Sequoia and observe a trade-off between the validity of predicted structures and the quality of predictions
The research has a purpose of revealing the specifics of the German emotional concept SEHNSUCHT on the basis of corpus-based method being one of the fragments of contrastive linguo-cultural (language-and-culture oriented) analysis of specific linguo-cultural (language-and-culture) concepts. The tested method includes two research procedures: 1) establishing relevant senses of the concept SEHNSUCHT and identifying their basic sense clusters by contrastive translation analysis of concordances built on the basis of the word query Sehnsucht; 2) determining the emotional concepts that can serve as the representatives of the concept SEHNSUCHT in the target linguo-cultures (languages and cultures). The latter procedure includes processing the co-occurrent profile of the word query Sehnsucht. This profile is an up-to-date definition of the lexeme Sehnsucht. By extrapolating the basic semantic features of this definition on the cognitive features of the concept SEHNSUCHT, the main concept representatives of the latter have been determined. It has been revealed that the basic emotional senses of the concept SEHNSUCHT (‘striving’, ‘desire’, ‘wish’, ‘longing’, ‘mourning for a person one loses’, ‘passionate attraction’ and others) create the following sense clusters: 1) ‘intensive inner affection’; 2) ‘passionate (sexual) affection’; 3) ‘striving for life changes (alternatives)’; 4) ‘longing (nostalgia) for life changes (alternatives)’; 5) ‘mourning (grief) for another person, often with no hope’. Based on the linguistic corpus statistic data as of the frequency of co-occurrent-like forms, co-occurrents and left- and right-hand collocates of the word query Sehnsucht, the co-occurrent profile of the word was developed. With the help of the relevant definition of the lexeme Sehnsucht studied on the basis of the co-occurrent profile, the basic concept representatives of the concept SEHNSUCHT, i.e. PASSION, DESIRE, SADNESS, were revealed. These concepts can facilitate an adequate transfer of the specific concept SEHNSUCHT to the target languages and cultures, provided no adequate analogue exists. The tested method may be also applied in psycholinguistic studies aimed at mental and verbal categorization of specific emotions. References Adolphs, S. (2006). Introducing Electronic Text Analysis: A practical guide for language and literary studies. London: Routledge. https://doi.org/10.4324/9780203087701 Bahn, D., Kauschke, Chr., Vesker, M., & Schwaryer, G. (2018). Perception of Valence and Arousal in German Emotion Terms: A Comparison between 9-year-old Children and Adults. Applied Psycholinguistics, 39(3), 463–481. https://doi.org/10.1017/S0142716417000443 Belica, C. (2011). Semantische Nähe als Ähnlichkeit von Kookkurrenzprofilen. In A. Abel & R. Zanin (Hrsg.), Korpora in Lehre und Forschung (pp. 155–178). Bozen-Bolzaro: Freie Universität. Bloch, R. (1967). Über die Bedeutung der Todessehnsucht für psychogene Störungen des Ernährungstriebes. Zeitschrift für Psychosomatische Medizin und Psychoanalyse, 13(1), 63–69. Citron, F., Weekes, B. S., & Ferstl, E. C. (2014). How are Affective Word Ratings Related to Lexicosemantic Properties? Evidence from the Sussex Affective Word List. Applied Psycholinguistics, 35, 313–331. doi:10.1017/S0142716412000409 Cowen, A. S., & Keltner, D. (2017). Self-report Captures 27 Distinct Categories of Emotion Bridged by Continuous Gradients. Proceedings of the National Academy of Sciences, 114(38), 7900–7909. https://doi.org/10.1073/pnas.1702247114 DKW. (1997). Der kleine Wahrig. Wörterbuch der deutschen Sprache. Gütersloh: Berstelsmann Lexikon. Du, S., Tao, Y., & Martinez, A. M. (2014). Compound Facial Expressions of Emotion. Proceedings of the National Academy of Sciences, 111(15), 1454–1462. https://doi.org/10.1073/pnas.1322355111 DWDS Digitales Wörterbuch der deutschen Sprache. Retrieved from: http://www.dwds.de/ressourcen/korpora/ Edgar, A., & Sedgwick, P., (eds). (2007). Cultural Theory: The Key Concepts. London: Routledge. Garnham, A. (1985). Psycholinguistics: Central Topics. London & New York: Methuen. Gawda, B. (2019). The Structure of the Concepts Related to Love Spectrum: Emotional Verbal Fluency Technique Application, Initial Psychometrics, and Its Validation. Journal of Psycholinguistic Research, 48, 1339–1361. https://doi.org./10.1007/s10936-019-09661-y Izard, C. E. (2011). Forms and Functions of Emotions: Matters of Emotion–Cognition Interactions. Emotion Review, 3, 371–378. https://doi.org/10.1177/1754073911410737 Каліщук Д., Лазука О. Особливості вербалізації концепту freedom в американському політичному дискурсі доби «холодної війни». East European Journal of Psycholinguistics, 2015. Т. 2, № 1, С. 52–58. Kotter-Grühn, D., Scheibe, S., Blanchard-Fields, F. & Baltes, P. B. (2009). Developmental Emergence and Functionality of “Sehnsucht” (Life Longings): The Sample Case of Involuntary Childlessness in Middle-aged Women. Psychology and Aging, 24(3), 634–644. https://doi.org/10.1037/a0016359 Kövecses, Z. 1990. Emotion Concepts. New York: Springer. Kulpina, V., & Tatarinov, V. (2018). Dictionary of Linguoculturology Terms as a Solution to the Current Research Problem. Open Journal for Studies in Linguistics, 1(1), 15–20. doi.org/10.32591/coas.ojsl.0101.02015k Kuperman, V., Estes, Z., Brysbaert, M., & Warriner, A. B. (2014). Emotion and Language: Valence and Arousal Affect Word Recognition. Journal of Experimental Psychology: General, 143, 1065–1081. doi:10.1037/a0035669. LCC Leipzig Corpora Collection: Deutsches Nachrichten-Korpus basierend auf Texten gecrawlt 2018. Retrieved from: http://corpora.uni-leipzig.de/de/res?word=Sehnsucht&corpusId=deu_newscrawl-public_2018 Levenson, R. W. (2011). Basic Emotion Questions. Emotion Review, 3, 379–386. https://doi.org/10.1177/1754073911410743 Ling. (2002). Sistema Elektronnyih Slovarey [System of Electronic Dictionaries]. ABBYY Lingvo 8.0. Software House. Lomas, T. (2016). Towards a Positive Cross-cultural Lexicography: Enriching Our Emotional Landscape through 216 “Untranslatable” Words Pertaining to Well-being. The Journal of Positive Psychology, 11(5), 546–558. doi: 10.1080/17439760.2015.1127993 Маслова, В. А. Когнитивная лингвистика. Минск: ТетраСистемс, 2005. Mayer, S., Scheibe, S. & Riediger, M. (2008). (Un)Reachable? An Empirical Differentiation of Goals and Life Longings. European Psychologist, 13(2), 126–140. doi: 10.1027/1016-9040.13.2.126 Mizin, K., Letiucha, L., & Petrov, O. (2019). Deutsche linguokulturelle Konzepte im Lichte der germanisch-ostslawischen Kontraste: Methode zur Feststellung von spezifischen bzw. einzigartigen Bedeutungen. Germanoslavica, 30(1), 49-70. Mizin, K., & Letiucha, L. (2019). The Linguo-Cultural Concept TORSCHLUSSPANIK as the Representative of Ethno-Specific Psycho-Emotional State of Germans. Psycholinguistics-Psiholingvistika, 25(2), 234–249. doi: 10.31470/2309-1797-2019-25-2 NWDTEL. (1993). New Webster’s Dictionary and Thesaurus of the English Language. Danbury CT: Lexicon Publications. Power, M. (2010). Emotion-Focused Cognitive Therapy. Chichester: Wiley. Scheibe, S., Freund, A. M., & Baltes, P. B. (2007). Toward a Developmental Psychology of Sehnsucht (Life Longings): The Optimal (Utopian) Life. Developmental Psychology, 43(3), 778–795. doi: 10.1037/0012-1649.43.3.778 Scheibe, S., Blanchard-Fields, F., Wiest, M. & Freund, A. M. (2011). Is Longing only for Germans? A Cross-cultural Comparison of Sehnsucht in Germany and the United States. Developmental Psychology, 47(3), 603–618. https://doi.org/10.1037/a0021807 Spiegel Online. (2004, October 25). “Habseligkeiten” ist schönstes deutsches Wort [“Belongings” is the most beautiful German word]. Retrieved from http://www.spiegel.de/kultur/gesellschaft/0,1518,324670,00.html Stillings, N. A., Chase, Chr. H., Weisler, S. E., Feinstein, M. H., Garfield, J. L., Rissland, E. L., & Weisler, S. W. (1995). Cognitive Science: An Introduction. Cambridge, MA: MIT Press. https://doi.org/10.1002/acp.2350030113 Talmy, L. (2007). Foreword. In M. Gonzalez-Marquez, I. Mittelberg, S. Coulson & M. J. Spivey (Eds.), Methods in Cognitive Linguistics (pp. 11–21). Amsterdam: John Benjamins. Vater, H. (2006). On the Mental Lexicon. Studi Linguistici e Filologici Online, 4(1), 175–204. Воркачев С. Г. Любов как лингвокультурный концепт. М.: Гнозис, 2007. Wierzbicka, A. (1999). Emotions Across Languages and Cultures: Diversity and Universals. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511521256 References (translated and transliterated) Adolphs, S. (2006). Introducing Electronic Text Analysis: A practical guide for language and literary studies. London: Routledge. https://doi.org/10.4324/9780203087701 Bahn, D., Kauschke, Chr., Vesker, M., & Schwaryer, G. (2018). Perception of valence and arousal in German emotion terms: A comparison between 9-year-old children and adults. Applied Psycholinguistics, 39(3), 463–481. https://doi.org/10.1017/S0142716417000443 Belica, C. (2011). Semantische Nähe als Ähnlichkeit von Kookkurrenzprofilen. In A. Abel & R. Zanin (Hrsg.), Korpora in Lehre und Forschung (pp. 155–178). Bozen-Bolzaro: Freie Universität. Bloch, R. (1967). Über die Bedeutung der Todessehnsucht für psychogene Störungen des Ernährungstriebes. Zeitschrift für Psychosomatische Medizin und Psychoanalyse, 13(1), 63–69. Citron, F., Weekes, B. S., & Ferstl, E. C. (2014). How are affective word ratings related to lexicosemantic properties? Evidence from the Sussex Affective Word List. Applied Psycholinguistics, 35, 313–331. doi:10.1017/S0142716412000409 Cowen, A. S., & Keltner, D. (2017). Self-report captures 27 distinct categories of emotion bridged by continuous gradients. Proceedings of the National Academy of Sciences, 114(38), 7900–7909. https://doi.org/10.1073/pnas.1702247114 DKW. (1997). Der kleine Wahrig. Wörterbuch der deutschen Sprache. Gütersloh: Berstelsmann Lexiko
Musical grammar describes a set of principles that are used to understand and interpret the structure of a piece according to a musical style. The main topic of this study is grammar induction for harmony --- the process of learning structural principles from the observation of chord sequences. The question how grammars are learnable by induction from sequential data is an instance of the more general question how abstract knowledge is inducible from the observation of data --- a central question of cognitive science. Under the assumption that human learning approximately follows the principles of rational reasoning, Bayesian models of cognition can be used to simulate learning processes. This study investigates what prior knowledge makes it possible to learn musical grammar inductively from Jazz chord sequences using Bayesian models and computational simulations. The theoretical part of the thesis presents how questions about learnability can be studied in a unified framework involving music analysis, cognitive modeling, Bayesian statistics, and computational simulations. A new grammar formalism, called Probabilistic Abstract Context-Free Grammar (PACFG), is proposed that allows for flexible probability models which facilitate the grammar-induction experiments of this study. PACFG can jointly model multiple musical dimensions such as harmony and rhythm, and can use coordinate ascent variational inference for grammar learning. The empirical part of the thesis reports supervised and unsupervised grammar-learning experiments. To train and evaluate grammar models, a ground-truth dataset of hierarchical analyses of complete Jazz standards, called the Jazz Harmony Treebank (JHT), was created. The supervised grammar-learning experiments, in which grammars for Jazz harmony are learned from the JHT analyses, show that jointly modeling harmony and rhythm significantly improves the grammar models' prediction of the ground truth. The performance and robustness of the grammars are further improved by a transpositionally invariant parameterization of rule probabilities. Following the supervised grammar learning, unsupervised grammar learning was performed by inducing harmony grammars merely from Jazz chord sequences, without the observation of the JHT trees. The results show that the best induced grammar performs similarly well as the best supervised grammar. In particular, the goal-directedness of functional harmony does not need to be assumed a priori, but can be learned without usage of music-specific prior knowledge. The findings of this thesis show that general prior knowledge enables an ideal learner to acquire abstract musical principles by statistical learning. In conclusion, it is plausible that much aspects of musical grammar have been learned by Jazz musicians and listeners, instead of being innate predispositions or explicitly taught concepts. This thesis is moreover embedded into the context of empirical music research and digital humanities. Current studies either describe complex musical structures qualitatively or investigate simpler aspects quantitatively. The computational models developed in this thesis demonstrate that deep insights into music and statistical analyses are not mutually exclusive. They enable a new kind of data-driven music theory and musicology, for instance through comparative analyses of musical grammar for different styles such as Jazz, Rock, and Western classical music.
The authors explored the experiences of adults living with a traumatic brain injury (TBI) in group art therapy. Integral to this study was a replicable methodology, including an original group art therapy treatment protocol for research that may be useful for practice-based applications. The treatment protocol consisted of a selected series of therapeutic art exercises for a short-term, 12-session art therapy course of treatment. Thirteen adults living with a TBI participated in the study, divided into two treatment groups comprised of six and seven adults (four female, nine male), respectively. Several quantitative measures and assessment tools were chosen to rate pre-therapy and post-therapy differences in self-esteem, coping, cognitive capacity, and affect. These included the Neurobehavioral Cognitive Status Examination (or Cognistat), the Coopersmith Self-Esteem Inventory (adult form), Rosenberg’s Self Esteem Scale, the COPE Inventory, and the Affect Rating Scale. The study included a pre-intervention interview, the Diagnostic Drawing Series (DDS), a comparative analysis of formal characteristics of the artwork, student observation notes, commentary on select imagery illustrating therapeutic transformations within the treatment process, and a satisfaction survey. Analyses of the quantitative data revealed a significant reduction in anxiety with positive clinical trends noted in domains of affect, cognitive functioning, and coping styles. The authors concluded that the study should be replicated with additional variables included.
Ignoring or withholding a response from a stimulus causes it to become affectively devalued. Leading accounts posit that this is due to negative affect elicited by neurocognitive inhibition when it is applied to resolve conflict from distracting or otherwise inappropriate stimulus/response representations. Other research, however, suggests that stimulus/response conflict may itself elicit negative affect and devalue stimuli, raising questions about whether effects previously attributed to inhibition may instead reflect the emotional impact of conflict, per se. To address this, we measured affective ratings of art-like patterns that previously appeared on critical trials of a task-switching paradigm (ABA vs. CBA task sequences) known for its capacity to distinguish behavioural effects of inhibition and conflict. Stimuli from the ABA-sequence experimental condition showing behavioural evidence of backward inhibition (n-2 repetition costs) received more negative ratings than those from the CBA-sequence control condition. This stimulus-devaluation effect was not impacted by the level of conflict associated with high uncertainty or low uncertainty about upcoming task order. Moreover, the response-time index of inhibition was larger on ABA trials in which the associated stimuli later received negative ratings than on trials preceding relatively positive ratings. Inhibition therefore appears to have negative affective consequences that exceed any emotional impact of conflict, with fluctuations in inhibition linked to subsequent stimulus evaluations.
Abstract Adapting threat-related memories towards changing environments is a fundamental ability of organisms. One central process of fear reduction is suggested to be extinction learning, experimentally modeled by extinction training that is repeated exposure to a previously conditioned stimulus (CS) without providing the expected negative consequence (unconditioned stimulus, US). Although extinction training is well investigated, evidence regarding process-related changes in neural activation over time is still missing. Using optimized delayed extinction training in a multicentric trial we tested whether: 1) extinction training elicited decreasing CS-specific neural activation and subjective ratings, 2) extinguished conditioned fear would return after presentation of the US (reinstatement), and 3) results are comparable across different assessment sites and repeated measures. We included 100 healthy subjects (measured twice, 13-week-interval) from six sites. 24h after fear acquisition training, extinction training, including a reinstatement test, was applied during fMRI. Alongside, participants had to rate subjective US-expectancy, arousal and valence. In the course of the extinction training, we found decreasing neural activation in the insula and cingulate cortex as well as decreasing US-expectancy, arousal and negative valence towards CS+. Re-exposure to the US after extinction training was associated with a temporary increase in neural activation in the anterior cingulate cortex (exploratory analysis) and changes in US-expectancy and arousal ratings. While ICCs-values were low, findings from small groups suggest highly consistent effects across time-points and sites. Therefore, this delayed extinction fMRI-paradigm provides a solid basis for the investigation of differences in neural fear-related mechanisms as a function of anxiety-pathology and exposure-based treatment. Clinical Trials Registration Registry names: Deutsches Register Klinischer Studien (DRKS) – German Clinical Trails Register ClinicalTrials.gov Registration ID-numbers: DRKS00008743 DRKS00009687 ClinicalTrials.gov Identifier: NCT02605668 URLs: https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00008743 https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00009687 https://clinicaltrials.gov/ct2/show/NCT02605668
Individuals with autism spectrum disorders (ASD) demonstrate increased visual attention and elevated brain reward circuitry responses to images related to circumscribed interests (CI), suggesting that a heightened affective response to CI may underlie their disproportionate salience and reward value in ASD. To determine if individuals with ASD differ from typically developing (TD) adults in their subjective emotional experience of CI object images, non-CI object images and social images, 213 TD adults and 56 adults with ASD provided arousal ratings (sensation of being energized varying along a dimension from calm to excited) and valence ratings (emotionality varying along dimension of approach to withdrawal) for a series of 114 images derived from previous research on CI. The groups did not differ on arousal ratings for any image type, but ASD adults provided higher valence ratings than TD adults for CI-related images, and lower valence ratings for social images. Even after co-varying the effects of sex, the ASD group, but not the TD group, gave higher valence ratings to CI images than social images. These findings provide additional evidence that ASD is characterized by a preference for certain categories of non-social objects and a reduced preference for social stimuli, and support the dissemination of this image set for examining aspects of the circumscribed interest phenotype in ASD.
An automatically derived AMR annotation layer of the FullStack multi-layer text corpus of Latvian. First, Latvian UD Treebank (v2.5) sentences were translated to English using a state-of-the-art Latvian-English neural MT system (Hugo.lv). Second, a state-of-the-art AMR parser for English (AMREager) was applied to the MT-translated sentences. Additionally, alignment information is provided for both Latvian-English translations and English-AMR parses.
In this study, it is our objective to carry out a historical tour of the main antecedents that we can find on the linguistic theories in Ferdinand de Saussure, with special emphasis on the influences he took for the elaboration of his theory of the sign. To do this, given the philosophicalrationalist nature that supports his theoretical conceptions, we are going to study the hypotheses preceding his, which had a logical-speculative nature. In this sense, we will start with Classical Antiquity focusing on the contributions made by the main Greek philosophers (Socrates, Platon and Aristotle) on the language / thought duality and the origin motivated or not of linguistic signs. Next, we will address the medieval theories of scholasticism and its conception of language as a syntactic and paradigmatic system in which agreement and rection were of fundamental importance, as Saussure would explain centuries later, categorizing language as a formal and functional system. Next, we will carry out an overview of the rationalist linguistic thought conceived by El Brocense in the 16th century and made explicit in his Minerva. From him, Saussure would take the conception that reason was above any use or linguistic norm that tried to limit language. Later, already located in the seventeenth century, we will study the general and reasoned Grammar of Port-Royal and its influence on Ferdinand de Saussure, especially with regard to the conception of the two faces of the linguistic sign (meaning and signifier). Finally, we will review some of the late nineteenth century theories that influenced Saussure and that were basically those conceived by the Kazan and Moscow schools and by the thought of the American linguist W. D. Whitney. Finally, we will expose some of the fundamental concepts contained in Ferdinand de Saussure's General Linguistics Course in which he presented his linguistic theories
Background: Obesity is a chronic disease characterized by a reduction in life expectancy. Bariatric surgery has been an alternative to conventional treatments, but lifestyle changes such as increased physical activity are crucial for achieving body image and health outcomes. Objective: Test the hypothesis that physical activity is associated with satisfaction with body image in obese individuals undergoing bariatric surgery. Methods: Cross-sectional study conducted in Salvador, BA, Brazil. All participants at 3 and 6 months postoperatively answered the Stunkard Image Rating Scales as the main outcome, in addition to the International Physical Activity Questionnaire to assess the physical activity profile. Results: Physically active individuals were 80% more likely to report body satisfaction when compared with physically inactive individuals. According to multivariate analysis, the adjusted physical activity for females and advanced age increased by 83% the chance of reporting satisfaction with body image and, when adjusted for marital status, body mass index, and surgery time, the strength of association increased to 89% the chance to refer body satisfaction when compared with physically inactive people. Conclusion: Physical activity was associated with better body image. These results indicate opportunities to improve outcomes in patients after bariatric surgery through counseling and treatment intervention.
Background: Youth with anxiety disorders struggle with managing emotions relative to peers, but the neural basis of this difference has not been examined. Methods: Youth (Mage=13.6; range=8-17) with (n=37) and without (n=24) anxiety disorders completed a cognitive reappraisal task while undergoing fMRI. Emotional reactivity and regulation, functional activation, and beta-series connectivity were compared across groups. Results: Groups did not differ on emotional reactivity or regulation. However, affect ratings and fronto-limbic activation after viewing aversive imagery (with and without regulation) were higher for anxious youth. Anxious youth did not demonstrate age-dependent changes in regulation, whereas regulation in control youth increased linearly. Stronger amygdala-vmPFC connectivity related to greater anxiety in control youth, but less anxiety in anxious youth. Stronger amygdala-frontal pole connectivity related to worse emotion regulation in control youth, but better emotion regulation in anxious youth. Conclusions: Anxious youth regulate when instructed, but this does not relate to age. Viewing aversive imagery related to heightened negative affect even after reappraisal, accompanied by higher fronto-limbic activation. Emotion dysregulation in youth anxiety disorders may stem from heightened emotionality and potent bottom-up neurobiological responses to aversive stimuli. Findings suggest the importance of treatments focused on both reducing initial emotional reactivity and bolstering regulatory capacity.
Fully data-driven, deep learning-based models are usually designed as language-independent and have been shown to be successful for many natural language processing tasks. However, when the studied language is low-resourced and the amount of training data is insufficient, these models can benefit from the integration of natural language grammar-based information. We propose two approaches to dependency parsing especially for languages with restricted amount of training data. Our first approach combines a state-of-the-art deep learning-based parser with a rule-based approach and the second one incorporates morphological information into the parser. In the rule-based approach, the parsing decisions made by the rules are encoded and concatenated with the vector representations of the input words as additional information to the deep network. The morphology-based approach proposes different methods to include the morphological structure of words into the parser network. Experiments are conducted on the IMST-UD Treebank and the results suggest that integration of explicit knowledge about the target language to a neural parser through a rule-based parsing system and morphological analysis leads to more accurate annotations and hence, increases the parsing performance in terms of attachment scores. The proposed methods are developed for Turkish, but can be adapted to other languages as well.
Universal Dependencies conversion of the Late Latin Charter Treebank 2
Abstract Kankana-ey is a widely used dialect in the northern region of the Philippines. Unfortunately, there are documented studies on the syntactic rules of this dialect. This study explored the development of a corpus for the Kankana-ey dialect. Further, the corpus was then used to establish the syntactic rules of Kankana-ey. A Kankana-ey version of the bible, dictionaries, news articles, songs and various online resources were used to collect words for the corpus of the Kankana-ey dialect. These identified words were also tagged using the parts of speech tags of the Penn TreeBank. Using the corpus and TensorFlow, 320 Kankana-ey sentences were analysed to determine the syntactic rules. In addition, 80 sentences were used to test the accuracy of the identified rules. At the end of the study, the created corpus has 3,412 tagged Kankana-ey words, while the analysis of the syntactic rules resulted to 1,722 rules. Testing also showed a 60% accuracy of the syntactic rules. In conclusion, the high number of identified rules from the 320 sentences was due to multiple Kankana-ey words having different possible tags. This also resulted to the low accuracy of the syntactic rules.
This article discusses one of the forms of machine translation, the Instagram translation feature called “see translation”. The research is focused on the translation techniques applied by the machine in translating Banyumas batik motifs from Indonesian to English found in @batikantodjamil and @batk_rd. This topic is worth discussing since machine translation is now getting more developed and is projected to replace human translator. However, in some cases, for example in dealing with culturally-bound terms, machine translation cannot perform contextual knowledge as well as the human translator. this mini research was conducted by applying qualitative research with purposive sampling technique in which the researchers obtain the data by selecting two batik center Instagram accounts containing batik motif names in the captions. The result shows that there are three translation techniques applied by the Instagram translation features, namely literal, borrowing, and particularization. The most dominant technique to use is borrowing technique, and it shows a tendency that such cultural terms in the source language do not have one-to-one correspondence in the target language. In other words, the touch of human translator is very important in the post-editing process of translation by machine to make the translation more acceptable. However, if it is impossible to involve human translator, the Instagram administrator should enrich the machine with more contextual linguistic database to provide the users with better translation results.
The Western Canadian Dictionary and Phrase-Book (WCD) was written in 1912 as a guide for British immigrants who were encountering a variety of English that was “more resistant to British linguistic norms than the conservative Anglophone heartland of Ontario” (Considine 2003: 252). Though the dictionary has been researched in terms of its lexicographical value, relatively little research has examined the historical and cultural reasons as to why a dictionary of western Canadian English was viable at the time it was written. This thesis examines the connections between the WCD and the Canadian Government’s pre-World War I immigration campaign. This includes connections between the writer of the dictionary, John Sandilands, and the Canadian Government through Sandilands position as a proofreader of pamphlets intended to advertise the West that were produced by the Department of the Interior. The thesis also examines how the dictionary participates in a network of literature produced at the time to reproduce a “Promised Land” (Francis 1989) narrative of the Canadian West which became “the dominant perception of the region during the formative years of agricultural settlement” (Francis & Kitzan 2007: IX). This network of literature includes stories by Nellie McClung and poetry by Robert J. C. Stead, who both employ Promised Land narratives in their work. Within these narratives a western Canadian dialect, marked by slang found in the WCD, becomes associated with a heightened morality of its speakers, and is used as a shorthand for values of hard work and honesty. Ultimately, it is argued that the dictionary reflects a dominant, settler, narrative of the West that was pushed by the Canadian Government to ‘sell’ the West.
Abstract Consumerism is an inherent feature of a modern consumer-minded society which enhances in some people both a hunger for collecting and a more serious desire developing into oniomania (shopping mania, shopaholism), kleptomania or pathological hoarding (syllogomania, Diogenes syndrome, Plyushkin’s syndrome). The paper proposes an interdisciplinary approach to the problem of pathological hoarding of unnecessary things and domestic animals by tenants of condominiums in Russian cities. Socio-legal prerequisites for this psychosocial disease still insufficiently studied in the country are also analyzed in the paper. The data of the Federal State Statistics Service of the Russian Federation and various legal acts were examined. In the course of the study, methods of quality content analysis and visual sociology were used to analyze cases of pathological hoarding highlighted in Russian digital media in recent years. The Clutter-Hoarding Scale and Clutter Image Rating Scale were used to interpret photos of cluttered Russian flats in condos. In conclusion, recommendations are given on improving state policy and the legislation of the Russian Federation.
First study in series of three tracking attention during encoding and subsequent memory accuracy after presenting misinformation. Study one using static images. Project covers eye tracking data, memory accuracy scores, mood data, valence and arousal ratings, study documentation and stimuli, assessment of central and peripheral information in visual medium.
Response Time Data and Valence Rating Data. <br>Covariate Tests: Big 5 Test, D2-R, MWT-B<br>Group 0 = Zen group, Group 1 = comparison group
Recurrent neural networks (RNNs) are a widely used deep architecture for\nsequence modeling, generation, and prediction. Despite success in applications\nsuch as machine translation and voice recognition, these stateful models have\nseveral critical shortcomings. Specifically, RNNs generalize poorly over very\nlong sequences, which limits their applicability to many important temporal\nprocessing and time series forecasting problems. For example, RNNs struggle in\nrecognizing complex context free languages (CFLs), never reaching 100% accuracy\non training. One way to address these shortcomings is to couple an RNN with an\nexternal, differentiable memory structure, such as a stack. However,\ndifferentiable memories in prior work have neither been extensively studied on\nCFLs nor tested on sequences longer than those seen in training. The few\nefforts that have studied them have shown that continuous differentiable memory\nstructures yield poor generalization for complex CFLs, making the RNN less\ninterpretable. In this paper, we improve the memory-augmented RNN with\nimportant architectural and state updating mechanisms that ensure that the\nmodel learns to properly balance the use of its latent states with external\nmemory. Our improved RNN models exhibit better generalization performance and\nare able to classify long strings generated by complex hierarchical context\nfree grammars (CFGs). We evaluate our models on CGGs, including the Dyck\nlanguages, as well as on the Penn Treebank language modelling task, and achieve\nstable, robust performance across these benchmarks. Furthermore, we show that\nonly our memory-augmented networks are capable of retaining memory for a longer\nduration up to strings of length 160.\n
How do each of us come to view the world uniquely? An emerging theory of microvalence proposes that subtle feelings of reward and punishment derived from individualized experiences with basic everyday objects help determine how we later attend and behave towards them. These objects that are part of our more mundane experiences are thought to be given attentional priority similar to objects that evoke stronger emotional responses. However, this relationship between preferences guided by daily experience and attention has not been tested. I introduced a novel paradigm to induce microvalences by simulating real life experience paired with an interocular suppression technique (bCFS) to explore its role in attention. Consistent with the theory of microvalence, affective ratings indicated that our novel shapes possessed pre-existing affective properties by which they are evaluated, giving rise to preferences. Unexpectedly, we observed a unifying effect of experience, blurring perceived differences between novel shapes, thus collapsing initial preferences (feelings of like or dislike). Results showed, however, that microvalences were not prioritized in attention. Our findings place emphasis on the role of experience in shifting automatic preferences to create unbiased representations of the world.
Acceleration and wide deployability in deeper recurrent neural network is hindered by high demand for computation and memory storage on devices with memory and latency constraints. In this work, we propose a novel regularization method to learn hardware-friendly sparse structures for deep recurrent neural networks. Considering the consistency of dimension in continuous time units in recurrent neural networks, low-rank structured sparse approximations of the weight matrices are learned through the regularization without dimension distortion. Our method is architecture agnostic and can learn compact models with higher degree of sparsity than the state-of-the-art structured sparsity learning method. The structured sparsity rather than random sparsity also facilitates the hardware implementation. Experiments on language modeling of Penn TreeBank dataset show that our approach can reduce the parameters of stacked recurrent neural network model by over 90% with less than 1% perplexity loss. It is also successfully evaluated on larger highway neural network model with word2vec dataset like enwik8 and text8 using only 20M weights.
A growing number of studies have shown that, compared to young adults, older adults better remember positive information than negative information. However, it is not clear whether this age-related positivity effect relies on an increase in positive information memory and/or on a decrease in negative information memory. Thus, we aimed to study the specific mechanisms underlying the age-related positivity effect in different memory tasks. To do so, we used an emotional word memory paradigm including immediate free recall, recognition and delayed free recall tasks. Forty-five young adults (m = 20.0 years) and 45 older adults (m = 69.2 years) native French speakers participated. Thirty-six low French words, including 12 negative (e.g. égout), 12 positive (e.g. lagune) and 12 neutral (e.g. notion) words were selected from an emotional lexical database (Gobin et al. 2017). For the recognition task, 36 new words were selected. The results showed that the age-related positivity effect specifically depended on a decrease in negativity preference (i.e. the comparison between negative and neutral words) in older adults, in comparison with young adults, both in immediate and delayed free recall tasks. Indeed, in these tasks, young adults recalled more negative than neutral words whereas there was no difference in older adults. In recognition task, no age-related positivity effect has been observed. Moreover, the results showed that, in immediate recall, the higher the older adults memory abilities, the lower their negativity preference. This correlation was not significant in delayed recall. These results suggest that, when compared with young adults, older adults disengage from negative words processing through costly cognitive processes. A small magnitude of negativity preference would indicate good maintenance of memory abilities. Results are discussed in the framework of the socioemotional selectivity theory.
Literary works are classified as works of imagination in the form of fictional or imaginary experiences. The messages to be conveyed through literary works must be creative so that they appear attractive to read and listen to, so it is necessary to have a stile from the authorship itself to make his work beautiful and attractive. There are many ways to enjoy, understand and appreciate the work of the author Tenas Effendy, one of which is by studying the Stile of Tenas Effendy's authorship in Tunjuk Ajar Melayu. This study aims to analyze and interpret Tenas Effendy's stile authorship in Tunjuk Ajar Melayu. This needs to be examined because the existence of a literary work can be seen from how the author packs his work so that he can create his own stile from the author's side. The method used in this research is content analysis method. The source of data in this research is Tunjuk Ajar Melayu Karya Tenas Effendy in 2013 which has been recorded. The data collection technique is done by applying the hermeneutic technique. After the author of the analysis, Tunjuk Ajar Melayu by Tenas Effendy has a unique and distinctive authorship stile seen from the stylistic aspect, namely the stile as a pack of thoughts, the stile as a deviation from linguistic norms and the stile as a collection of personal characteristics.
The article highlights monitoring and dynamics of language development in post-soviet Kyrgyzstan. The crucial politico-social changes after the collapse of the USSR affected greatly the language situation of the country. Hence, this article’s aim is to give some sociolinguistic analysis to the following issues: • To monitor country’s up-to date language situation • To speak on the new language policy of Kyrgyzstan in modern stage • To encounter new socio-economic and political challenges in the process of language lawmaking • To define the interrelationships between dominant languages and vernaculars of minority communities This article also explains the reasons of granting Russian language the status of Lingua Franca, immediately after Kyrgyzstan’s becoming an independent state. One of the acute problems today is corpus planning, which means codification of newly coined or borrowed words. The flow of new terminology from the other languages, especially “Americanisms” and “Englishisisms” which replaced so-called “Sovietisms”, needs to be standardized according to the linguistic norms of state (Kyrgyz) language. The article also reveals “hierarchical” disposition of main languages due to their functional load in 20 domains of Kyrgyzstan. For the last 28 years’ tremendous changes have happened in language space of Kyrgyzstan, directly touching upon the positions of Kyrgyz, Russian, Uzbek, English, Turkish and other languages. Therefore, language system of Kyrgyzstan nowadays presents complex, intertwined interrelations between all nationalities residing in Kyrgyzstan and language cooperation among them. It happened because of some socio-political reasons: 1) The flow of Russian speaking population from industrial areas to the Russian Federation. 2) Changing demographic situation on the country. 3) Inner immigration process when many unemployed people from the distant regions came to Bishkek and Chui valley to find job possibilities.
We analyse and explain the increased generalisation performance of iterate averaging using a Gaussian process perturbation model between the true and batch risk surface on the high dimensional quadratic. We derive three phenomena \latestEdits{from our theoretical results:} (1) The importance of combining iterate averaging (IA) with large learning rates and regularisation for improved regularisation. (2) Justification for less frequent averaging. (3) That we expect adaptive gradient methods to work equally well, or better, with iterate averaging than their non-adaptive counterparts. Inspired by these results\latestEdits{, together with} empirical investigations of the importance of appropriate regularisation for the solution diversity of the iterates, we propose two adaptive algorithms with iterate averaging. These give significantly better results compared to stochastic gradient descent (SGD), require less tuning and do not require early stopping or validation set monitoring. We showcase the efficacy of our approach on the CIFAR-10/100, ImageNet and Penn Treebank datasets on a variety of modern and classical network architectures.
Cultural priming studies frequently employ non-validated, stereotypical images. Here, we select images to separately evoke two cultural mindsets: Hispanic and US-American. Spanish-English bilinguals identifying as Hispanic/Latino (N=149) rated 50 images online for their cultural and emotional evocation. Based on relative cultural identification, cultural "delegate" (strongly US-American, strongly Hispanic, balanced bicultural) subsamples' ratings were averaged to isolate particularly salient images. Image ratings were compared across respondents' national origins. Ratings of seven selected pairs of content-matched Hispanic and US-American primes were compared across the full sample. High discrimination across cultural mindsets and positive emotion ratings were maintained regardless of various demographic factors. Thus, we provide empirical justification for incorporating these stimuli, individually or as sets, within cultural priming studies among Hispanic/Latino samples.
Semantic Role Labelling (SRL) is the process of automatically finding the semantic roles of terms in a sentence. It is an essential task towards creating a machine-meaningful representation of textual information. One public linguistic resource commonly used for this task is the FrameNet Project. FrameNet is a human and machine-readable lexical database containing a considerable number of annotated sentences, those annotations link sentence fragments to semantic frames. However, while the annotations across all the documents covered in the dataset link to most of the frames, a large group of frames lack annotations in the documents pointing to them. In this paper, we present a data augmentation method for FrameNet documents that increases by over 13% the total number of annotations. Our approach relies on lexical, syntactic, and semantic aspects of the sentences to provide additional annotations. We evaluate the proposed augmentation method by comparing the performance of a state-of-the-art semantic-role-labelling system, trained using a dataset with and without augmentation.
'Synonym' is an imperative instrument of commonsense knowledge that we apply to make a good sense and sound judgement of our reading. To investigate the ability of machine comprehension models in handling the synonym commonsense knowledge, we developed an innovative approach to automatically generate a dataset based on the Stanford Question Answering Dataset (SQuAD 2.0). The brand-new dataset consists of additional distracting sentences or questions spawned using synonym commonsense knowledge. We formulated new questions by replacing noun entities of the original ones in SQuAD 2.0 with their synonyms. This approach followed the two fundamental principles of SQuAD 2.0 dataset: relevancy and plausibility (incorrect answers are more challenging if they are relevant and plausible). It improves the robustness/abstraction of the question set. To improve the synonym selection strategy in Word Sense Disambiguation (WSD) problem, we designed a new algorithm Multiple Source Adapted Lesk Algorithm (MSALA). Rather than only using WordNet as the source of gloss for adapted Lesk algorithm, we used both lexical database WordNet and commonsense database ConceptNet. This fusion provides a rich hierarchy of semantic relations for the MSALA algorithm. Using this method, we devised 11,000 questions and evaluated the performance of the state-of-the-art question answering system-BERT. Our result shows that the accuracy of the contemporary BERT-Base model dropped from 74.98% to 63.24%. This 10+% accuracy drop revealed the limitations of BERT in handling synonym commonsense knowledge.
Abstract Exaggerated reactivity to drug-cues and emotional dysregulations represent key symptoms of early stages of substance use disorders. The diagnostic criteria for (Internet) Gaming Disorder strongly resemble symptoms for substance-related addictions. However, previous cross-sections studies revealed inconsistent results with respect to neural cue reactivity and emotional dysregulations in these populations. To this end the present fMRI study applied a combined cross-sectional and prospective longitudinal design in excessive online gamers (n=37) and gaming-naïve controls (n=67). To separate gaming-associated changes from predisposing factors, gaming-naive subjects were randomly assigned to 6 weeks of daily Internet gaming or a non-gaming condition. At baseline and after the training subjects underwent an fMRI paradigm presenting gaming-related cues and non-gaming related emotional stimuli. Cross-sectional comparisons revealed gaming-cue specific enhanced valence attribution and neural reactivity in a parietal network, including the posterior cingulate/precuneus in excessive gamers as compared to gaming naïve-controls. Prospective analysis revealed that six weeks of gaming elevated valence ratings as well as neural cue-reactivity in a similar parietal network, specifically the posterior cingulate/precuneus in previously gaming-naïve controls. Together, the prospective longitudinal design did not reveal supporting evidence for altered emotional processing of non-gaming associated stimuli in excessive gamers while convergent evidence for increased emotional and neural reactivity to gaming-associated stimuli was observed. Findings suggest that exaggerated neural reactivity in posterior parietal regions engaged in self-referential processing already occur during early stages of regular gaming probably promoting continued engagement in gaming behavior.
Abstract In this paper, A shorter version of the paper appeared in German in the final report of the Digital Plato project which was funded by the Volkswagen Foundation from 2016 to 2019. [35], [28]. we present a method for paraphrase extraction in Ancient Greek that can be applied to huge text corpora in interactive humanities applications. Since lexical databases and POS tagging are either unavailable or do not achieve sufficient accuracy for ancient languages, our approach is based on pure word embeddings and the word mover’s distance (WMD) [20]. We show how to adapt the WMD approach to paraphrase searching such that the expensive WMD computation has to be computed for a small fraction of the text segments contained in the corpus, only. Formally, the time complexity will be reduced from <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:mi>O</m:mi><m:mo>(</m:mo><m:mi>N</m:mi><m:mo>·</m:mo><m:msup><m:mrow><m:mi>K</m:mi></m:mrow><m:mrow><m:mn>3</m:mn></m:mrow></m:msup><m:mo>·</m:mo><m:mo>log</m:mo><m:mi>K</m:mi><m:mo>)</m:mo></m:math> \mathcal{O}(N\cdot {K^{3}}\cdot \log K) to <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:mi>O</m:mi><m:mo>(</m:mo><m:mi>N</m:mi><m:mo>+</m:mo><m:msup><m:mrow><m:mi>K</m:mi></m:mrow><m:mrow><m:mn>3</m:mn></m:mrow></m:msup><m:mo>·</m:mo><m:mo>log</m:mo><m:mi>K</m:mi><m:mo>)</m:mo></m:math> \mathcal{O}(N+{K^{3}}\cdot \log K), compared to the brute-force approach which computes the WMD between each text segment of the corpus and the search query. N is the length of the corpus and K the size of its vocabulary. The method, which searches not only for paraphrases of the same length as the search query but also for paraphrases of varying lengths, was evaluated on the Thesaurus Linguae Graecae ® (TLG ® ) [25]. The TLG consists of about <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"><m:mn>75</m:mn><m:mo>·</m:mo><m:msup><m:mrow><m:mn>10</m:mn></m:mrow><m:mrow><m:mn>6</m:mn></m:mrow></m:msup></m:math> 75\cdot {10^{6}} Greek words. We searched the whole TLG for paraphrases for given passages of Plato. The experimental results show that our method and the brute-force approach, with only very few exceptions, propose the same text passages in the TLG as possible paraphrases. The computation times of our method are in a range that allows its application in interactive systems and let the humanities scholars work productively and smoothly.
With the popularity of the Internet, the public can get news from the recent hottest news, and express their opinion in time in the network of social media, such as microblog, twitter. With the help of sentiment analysis of the comments, the government or the media can inform of the public opinion, and make corresponding decisions, so that they are able to get the positive feedback. Also, the sentiment analysis can help people block the detrimental comments. Since the sentiment analysis is useful in the daily life, the author made an experiment about the sentiment analysis of three models, namely, Naive Byes, Maximum Entropy, and SVM, to compare the results' accuracy of them. And the dataset used in this experiment is Stanford Twitter Sentiment (STS). Besides, the reference is made to Stanford Sentiment Treebank and IMDB. In addition, the determination of the emotional tone is based on emotional dictionaries like GI (General Inquirer) and How Net. By comparing the accuracy and training time of different models, SVM is selected to be the optimal model with the F-scores of 82%.
In this paper, we present results of employing DBpedia and YAGO as lexical databases for answering questions formulated in the natural language. The proposed solution has been evaluated for answering class 1 and class 2 questions (out of 5 classes defined by Moldovan for TREC conference). Our method uses dependency trees generated from the user query. The trees are browsed for paths leading from the root of the tree to the question subject. We call those paths fibers and they represent the user intention. The question analysis consists of three stages: query analysis, query breakdown and information retrieval. The aim of those stages is the detection of the entities of interest and its attributes, indicating the users’ domain of interest. The user query is then converted into a SPARQL query and sent to the DBpedia and YAGO databases. The baseline and the extended methods are presented and the quality of the solution is evaluated and discussed.
Music’s ability to influence exercise performance is well known, but the converse, how exercise influences music listening, remains largely unknown. Exercise can elevate arousal, mood, and neurotransmitters including dopamine, which are involved in musical pleasure. Here we examine how exercise influences music enjoyment, and test for a modulatory role of arousal, mood, and dopamine. Before and after exercise (12 min of vigorous running) and a rest control session, participants (n=20) listened to music clips and rated their enjoyment and subjective arousal; we also collected mood ratings and eye-blink rates, an established predictor of dopamine activity. Ratings of musical enjoyment increased significantly after running, but not after the rest control condition. While changes in subjective arousal ratings did not differ between run and rest days, change in subjective arousal correlated with change in music enjoyment. After running, the change in music enjoyment had a positive but non-significant correlation with change in eye- blink rates (r=.36). Positive mood increased more after exercise than after the rest control session, but the change in positive mood did not correlate with change in music enjoyment. In sum, exercise leads to increased musical pleasure, and this effect was related to changes in arousal.
This paper presents the first treebank for the Laz language, which is also the first Universal Dependencies Treebank for a South Caucasian language. This treebank aims to create a syntactically and morphologically annotated resource for further research. We also aim to document an endangered language in a systematic fashion within an inherently cross-linguistic framework: the Universal Dependencies Project (UD). As of now, our treebank consists of 576 sentences and 2,306 tokens annotated in light with the UD guidelines. We evaluated the treebank on the dependency parsing task using a pretrained multilingual parsing model, and the results are comparable with other low-resourced treebanks with no training set. We aim to expand our treebank in the near future to include 1,500 sentences. The bigger goal for our project is to create a set of treebanks for minority languages in Anatolia.
Electroencephalography (EEG)-based emotion recognition has advanced the field in affective computing and has enabled applications in human-computer interactions. Despite significant progress has been made in decoding emotion using supervised machine-learning methods, few studies applied data-driven, unsupervised approaches to explore the underlying EEG dynamics during an emotion experiment and examine how such dynamics correlate with subjective reports of emotion. This study employs the adaptive mixture independent component analysis (AMICA), an unsupervised approach, to EEG data from the DEAP dataset where 32 subjects watched emotional videos. Empirical results showed that AMICA could learn distinct models that separated EEG date collected in the emotion experiment. The identified changes in EEG patterns were weakly-correlated with the four reported emotion scales, indicating the underlying EEG dynamics partially reflected the emotional activities as well as the emotion-irrelevant brain dynamics. Further, the correlations between EEG dynamics and individuals' subjective emotional ratings were significantly higher than those between the EEG and the average ratings from online raters. Finally, building an emotion-decoding model based on the EEG dynamics revealed a significantly better classification performance for valence ratings compared to arousal. This study demonstrated the use of AMICA in characterizing the EEG dynamics in emotion experiments and provided insight into the relationship between EEG and the reported emotional experiences. The unsupervised learning approach can be applied to studying emotion and other confounding factors such as emotion irrelevant EEG artifacts, thereby improving the performance of emotion decoding for EEG-based affective computing.
The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers. However, the NLP literature has missed an important difference between the two structures: only one edge may emanate from the root in a dependency tree. We analyzed the output of state-of-the-art parsers on many languages from the Universal Dependency Treebank: although these parsers are often able to learn that trees which violate the constraint should be assigned lower probabilities, their ability to do so unsurprisingly de-grades as the size of the training set decreases. In fact, the worst constraint-violation rate we observe is 24%. Prior work has proposed an inefficient algorithm to enforce the constraint, which adds a factor of n to the decoding runtime. We adapt an algorithm due to Gabow and Tarjan (1984) to dependency parsing, which satisfies the constraint without compromising the original runtime.
Abstract This article presents a novel method to determine particular syntactical attributes of Ancient Greek oratory to quantitatively compare the orators of the Classical to those of the Imperial era based on their style of writing. The study first provides a philological overview of Classical Atticism and its Imperial counterpart and argues that the latter is the product of creative mimēsis and not a mere reproduction of archetypes. Then the article briefly explains a node-based metric method that was developed to quantify the morphology of a syntactically annotated Treebank that led to a more thorough weighting scheme using Haar Wavelets. Wavelets were then used to capture both the linear topology of a sentence and the tree network topology of the corresponding syntactical tree. The results were subsequently processed using principal component analysis to analyze and visualize the data. The method is demonstrated using a database of syntactically annotated sentences from six Attic orators.
We present a method for conducting morphological disambiguation for South S\'ami, which is an endangered language. Our method uses an FST-based morphological analyzer to produce an ambiguous set of morphological readings for each word in a sentence. These readings are disambiguated with a Bi-RNN model trained on the related North S\'ami UD Treebank and some synthetically generated South S\'ami data. The disambiguation is done on the level of morphological tags ignoring word forms and lemmas; this makes it possible to use North S\'ami training data for South S\'ami without the need for a bilingual dictionary or aligned word embeddings. Our approach requires only minimal resources for South S\'ami, which makes it usable and applicable in the contexts of any other endangered language as well.
The performance of a long short-term memory (LSTM) recurrent neural network (RNN)-based language model has been improved on language model benchmarks. Although a recurrent layer has been widely used, previous studies showed that an LSTM RNN-based language model (LM) cannot overcome the limitation of the context length. To train LMs on longer sequences, attention mechanism-based models have recently been used. In this paper, we propose a LM using a neural Turing machine (NTM) architecture based on localized content-based addressing (LCA). The NTM architecture is one of the attention-based model. However, the NTM encounters a problem with content-based addressing because all memory addresses need to be accessed for calculating cosine similarities. To address this problem, we propose an LCA method. The LCA method searches for the maximum of all cosine similarities generated from all memory addresses. Next, a specific memory area including the selected memory address is normalized with the softmax function. The LCA method is applied to pre-trained NTM-based LM during the test stage. The proposed architecture is evaluated on Penn Treebank and enwik8 LM tasks. The experimental results indicate that the proposed approach outperforms the previous NTM architecture.
Text discourse parsing plays an important role in understanding information flow and argumentative structure in natural language. Previous research under the Rhetorical Structure Theory (RST) has mostly focused on inducing and evaluating models from the English treebank. However, the parsing tasks for other languages such as German, Dutch, and Portuguese are still challenging due to the shortage of annotated data. In this work, we investigate two approaches to establish a neural, cross-lingual discourse parser via: (1) utilizing multilingual vector representations; and (2) adopting segment-level translation of the source content. Experiment results show that both methods are effective even with limited training data, and achieve state-of-the-art performance on cross-lingual, document-level discourse parsing on all sub-tasks.
This paper investigates whether typical stress patterns in English nouns and verbs are available as a prosodic cue for categorisation and accelerated word learning during first language acquisition. The stress typicality hypothesis states that left-stressed nouns and right-stressed verbs should be acquired earlier than the reverse configurations if stress effectively signals lexical class membership. In this view, class-typical stress patterns are expected to facilitate learning of novel items. A series of generalized additive models (GAMs) based on a comprehensive set of lexical data (CELEX) as well as a large set of age-of-acquisition (AoA) and concreteness ratings reveals that stress typicality plays a minor role in early acquisition, as it is generally superseded by a preference for left-hand (or 'trochaic') patterns in both nouns and verbs. This may be explained by general cognitive constraints (perceptual salience and recency) or exposure to the dominant pattern in the ambient language.
An interesting and frequent type of multiword expression (MWE) is the headless MWE, for which there are no true internal syntactic dominance relations; examples include many named entities ("Wells Fargo") and dates ("July 5, 2020") as well as certain productive constructions ("blow for blow", "day after day").Despite their special status and prevalence, current dependency-annotation schemes require treating such flat structures as if they had internal syntactic heads, and most current parsers handle them in the same fashion as headed constructions.Meanwhile, outside the context of parsing, taggers are typically used for identifying MWEs, but taggers might benefit from structural information.We empirically compare these two common strategies-parsing and tagging-for predicting flat MWEs.Additionally, we propose an efficient joint decoding algorithm that combines scores from both strategies.Experimental results on the MWE-Aware English Dependency Corpus and on six non-English dependency treebanks with frequent flat structures show that: (1) tagging is more accurate than parsing for identifying flat-structure MWEs, (2) our joint decoder reconciles the two different views and, for non-BERT features, leads to higher accuracies, and (3) most of the gains result from feature sharing between the parsers and taggers.
Deep learning has promoted remarkable progress in various tasks while the effort devoted to these hand-crafting neural networks has motivated so-called neural architecture search (NAS) to discover them automatically. Recent aging evolution (AE) automatic search algorithm turns to discard the oldest model in population and finds image classifiers beyond manual design. However, it achieves a low speed of convergence. A nonaging evolution (NAE) algorithm tends to neglect the worst architecture in population to accelerate the search process whereas it obtains a lower performance compared with AE. To address this issue, in this letter, we propose to use an optimized evolution algorithm for recurrent NAS (EvoRNAS) by setting a probability ϵ to remove the worst or oldest model in population alternatively, which can balance the performance and time length. Besides, parameter sharing mechanism is introduced in our approach due to the heavy cost of evaluating the candidate models in both AE and NAE. Furthermore, we train the sharing parameters only once instead of many epochs like ENAS, which makes the evaluation of candidate models faster. On Penn Treebank, we first explore different ϵ in EvoRNAS and find the best value suited for the learning task, which is also better than AE and NAE. Second, the optimal cell found by EvoRNAS can achieve state-of-the-art performance within only 0.6 GPU hours, which is 20 × and 40 × faster than ENAS and DARTS. Moreover, the transferability of the learned architecture to WikiText-2 also shows strong performance compared with ENAS or DARTS.
Sentiment analysis, especially for long documents, plausibly requires methods\ncapturing complex linguistics structures. To accommodate this, we propose a\nnovel framework to exploit task-related discourse for the task of sentiment\nanalysis. More specifically, we are combining the large-scale,\nsentiment-dependent MEGA-DT treebank with a novel neural architecture for\nsentiment prediction, based on a hybrid TreeLSTM hierarchical attention model.\nExperiments show that our framework using sentiment-related discourse\naugmentations for sentiment prediction enhances the overall performance for\nlong documents, even beyond previous approaches using well-established\ndiscourse parsers trained on human annotated data. We show that a simple\nensemble approach can further enhance performance by selectively using\ndiscourse, depending on the document length.\n
In order to extract the semantic and grammatical information of sentences more effectively, this paper proposes a sentence sentiment classification method based on Self-supervised and Self-attention mechanism (SS-SAtt-BiLSTM). In this method, BiLSTM network is used to extract the feature of text context relationship, and self-supervised (SS) learning mode is introduced into the supervised sentence representation model. The sentence itself is used as the label data information of current words, and an improved self-attention mechanism (SA) is used to calculate the attention weight of each moment. The experimental results of MR and Stanford sentient treebank (sst-5) data sets show that this method reduces the dependence on tagged data, and the improved self-attention mechanism enables the model to learn more key features of sentences and improve the classification performance.