Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
Most of the existing sentiment classification models use Word2Vec, GloVe, etc. to obtain the word vector representation of the text. But these methods ignore the context of words. In response to this problem, a neural network model based on the combination of BERT (bidirectional encoder representations from transformers) pre-trained language model and BLSTM (bidirectional long short-term memory network) and attention mechanism is proposed for text sentiment analysis in this paper. First, the word vector which including contextual semantic information is obtained through the BERT pre-training model. Then this paper uses the two-way long and short-term memory network to extract context-related features for deep learning. Finally, the attention mechanism is introduced to assign weights to the extracted information, highlight the important information, and perform text sentiment classification. The test accuracy rate can reach 89.17% on the SST (stanford sentiment treebank) data set, which shows that this method has a certain degree of improvement in this type of accuracy compared with other methods.
Abstract The paper investigates formal language in persuasive discourse on the r /C hange M y V iew subreddit. We collected a corpus of 100 million messages, split into subcorpora based on the user-awarded marker delta, which rewards changing an original poster’s view. Assuming that formality/informality is potentially an important factor in the persuasiveness of a message, we examine the two subcorpora with respect to formality markers. The results indicate no systematic variation along the formality/informality continuum between persuasive and non-persuasive posts on r /C hange M y V iew. The posters use personal pronouns, suasive verbs, emphatics, imperatives, elaborate connectors and WH-questions with similar frequency, and express themselves using vocabulary and syntax of similar complexity. Moreover, keyword lists and n-gram rankings indicate no register difference. A qualitative analysis of concordance lines for persuade and change PRONOUN view paints a picture of a community that values factual, evidence-based discourse and openness to logical persuasion, with a linguistic norm of relatively formal, sophisticated register.
We present an approach for automatic punctuation restoration with BERT models for English and Hungarian. For English, we conduct our experiments on Ted Talks, a commonly used benchmark for punctuation restoration, while for Hungarian we evaluate our models on the Szeged Treebank dataset. Our best models achieve a macro-averaged $F_1$-score of 79.8 in English and 82.2 in Hungarian. Our code is publicly available.
Heightened responding to uncertain threat is considered a hallmark of anxiety disorder pathology. We sought to determine whether individual differences in self-reported intolerance of uncertainty (IU), a key transdiagnostic dimension in anxiety-related pathology, underlies differential recruitment of neural circuitry during cue-signalled uncertainty of threat (n = 42). In an instructed threat of shock task, cues signalled uncertain threat of shock (50%) or certain safety from shock. Ratings of arousal and valence, skin conductance response (SCR), and functional magnetic resonance imaging were acquired. Overall, participants displayed greater ratings of arousal and negative valence, SCR, and amygdala activation to uncertain threat versus safe cues. IU was not associated with greater arousal ratings, SCR, or amygdala activation to uncertain threat versus safe cues. However, we found that high IU was associated with greater ratings of negative valence and greater activity in the medial prefrontal cortex and dorsomedial rostral prefrontal cortex to uncertain threat versus safe cues. These findings suggest that during cue-signalled uncertainty of threat, individuals high in IU rate uncertain threat as aversive and engage prefrontal cortical regions known to be involved in safety-signalling and conscious threat appraisal. Taken together, these findings highlight the potential of IU in modulating safety-signalling and conscious appraisal mechanisms in situations with cue-signalled uncertainty of threat, which may be relevant to models of anxiety-related pathology.
Inferring emotions from Head Movement (HM) and Eye Movement (EM) data in 360° Virtual Reality (VR) can enable a low-cost means of improving users’ Quality of Experience. Correlations have been shown between retrospective emotions and HM, as well as EM when tested with static 360° images. In this early work, we investigate the relationship between momentary emotion self-reports and HM/EM in HMD-based 360° VR video watching. We draw on HM/EM data from a controlled study (N=32) where participants watched eight 1-minute 360° emotion-inducing video clips, and annotated their valence and arousal levels continuously in real-time. We analyzed HM/EM features across fine-grained emotion labels from video segments with varying lengths (5-60s), and found significant correlations between HM rotation data, as well as some EM features, with valence and arousal ratings. We show that fine-grained emotion labels provide greater insight into how HM/EM relate to emotions during HMD-based 360° VR video watching.
We present and evaluate the concept of FeelMusic and evaluate an implementation of it. It is an augmentation of music through the haptic translation of core musical elements. Music and touch are intrinsic modes of affective communication that are physically sensed. By projecting musical features such as rhythm and melody into the haptic domain, we can explore and enrich this embodied sensation; hence, we investigated audio-tactile mappings that successfully render emotive qualities. We began by investigating the affective qualities of vibrotactile stimuli through a psychophysical study with 20 participants using the circumplex model of affect. We found positive correlations between vibration frequency and arousal across participants, but correlations with valence were specific to the individual. We then developed novel FeelMusic mappings by translating key features of music samples and implementing them with “Pump-and-Vibe”, a wearable interface utilising fluidic actuation and vibration to generate dynamic haptic sensations. We conducted a preliminary investigation to evaluate the FeelMusic mappings by gathering 20 participants’ responses to the musical, tactile and combined stimuli, using valence ratings and descriptive words from Hevner’s adjective circle to measure affect. These mappings, and new tactile compositions, validated that FeelMusic interfaces have the potential to enrich musical experiences and be a means of affective communication in their own right. FeelMusic is a tangible realisation of the expression “feel the music”, enriching our musical experiences.
Cognitive reappraisal is an emotion regulation strategy to reduce the impact of affective stimuli. This regulation could be incomplete in patients with functional neurologic disorder (FND) resulting in an overflowing emotional stimulation perpetuating symptoms in FND patients. Here we employed functional MRI to study cognitive reappraisal in FND. A total of 24 FND patients and 24 healthy controls employed cognitive reappraisal while seeing emotional visual stimuli in the scanner. The Symptom Checklist-90-R (SCL-90-R) was used to evaluate concomitant psychopathologies of the patients. During cognitive reappraisal of negative IAPS images FND patients show an increased activation of the right amygdala compared to normal controls. We found no evidence of downregulation in the amygdala during reappraisal neither in the patients nor in the control group. The valence and arousal ratings of the IAPS images were similar across groups. However, a subgroup of patients showed a significant higher account of extreme low ratings for arousal for negative images. These low ratings correlated inversely with the item "anxiety" of the SCL-90-R. The increased activation of the amygdala during cognitive reappraisal suggests altered processing of emotional stimuli in this region in FND patients.
This paper introduces ABC Treebank, a general-purpose categorial grammar (CG) treebank for Japanese.It is 'general-purpose' in the sense that it is not tailored to a specific variant of CG, but rather aims to offer a theory-neutral linguistic resource (as much as possible) which can be converted to different versions of CG (specifically, CCG and Type-Logical Grammar) relatively easily.In terms of linguistic analysis, it improves over the existing Japanese CG treebank (Japanese CCGBank) on the treatment of certain linguistic phenomena (passives, causatives, and control/raising predicates) for which the lexical specification of the syntactic information reflecting local dependencies turns out to be crucial.In this paper, we describe the underlying 'theory' dubbed ABC Grammar that is taken as a basis for our treebank, outline the general construction of the corpus, and report on some preliminary results applying the treebank in a semantic parsing system for generating logical representations of sentences.
Manually annotated corpus is a perquisite for several natural language processing applications including parsing. Nevertheless, annotated corpus is not always available for resource-poor languages, especially when domain under consideration is noisy user-generated data found on social media platforms such as Twitter. To overcome this deficiency of hand-annotated corpus, researchers have focused their attention on semi-automatic corpus annotation methods. This paper describes the experiments carried out using semi-automatic methods like self-training and co-training in an attempt for creating silver-standard dependency treebank of Urdu tweets. Six iterations of each approach were performed using same experimental conditions using MaltParser and Parsito parser, both statistical data driven parsers. For self-training experiments, the best performing MaltParser model was trained on 1250 Urdu tweets, with an accuracy of 70.2% LA, 74.4% UAS, 63% LAS. Whereas the best performing Parsito model was also trained on 1250 Urdu tweets with an accuracy of 70.8% LA, 74.8% UAS, 63.4% LAS. For co-training experiments, best performing MaltParser model was trained on 1500 Urdu tweets, with an accuracy of 70.5% LA, 74.4% UAS, 63.2% LAS. The best performing Parsito model was also trained on 1500 Urdu tweets with an accuracy of 70.5% LA, 74.3% UAS, 63% LAS. Although, there was not much difference between the results of both approaches, co-training results were slightly better for both parsers and is used for generating a silver-standard dependency treebank of 4500 Urdu tweets.
The article explores the use of contextual slang as linguistic performance by three all-female friendship groups in Calabar metropolis, Cross River State, south-eastern Nigeria. I argue that slang constitutes critical components of the discursive practices of young urban Nigerian women in maintaining friendship and deviating from stereotyped cultural and linguistic norms. Drawing insights from the analytical tools of African feminism and linguistic ideology, the article discusses recurrent themes in young women’s contextual slanguage and the motivations for the use of these creative linguistic and cultural resources in defining participants’ authentic social selves and in enacting their different modes of belonging. Qualitative ethnographic data for the study were sourced from participant observations, semi-structured interviews and informal conversations with 30 participants. The study concludes that young urban women utilise contextual slang as indexical tools in their everyday narratives to negotiate meaning in relation to the experience of their social lives, to acculturate to male linguistic norms and to affiliate with ideologies that represent gendered identity.
This paper develops the concept of word order universals based on a data analysis of the Universal Dependencies project, which proposes treebanks of more than 90 languages encoded with the same annotation scheme. The nature of the data we work on allows us to extract rich details for testing well-known typological implicational universals and, further, explore new kinds of universals that we call quantitative universals. We show how such quantitative universals are in essence different from implicational universals, including statistical universals, by the fact that they no longer lay down any claims on categorical statements, but rather on continuous parameters, opening a new field of research we propose to call typometrics.
We propose a theoretical reflection on the functions of linguistic norms and the tensions between the linguistic centre(s) and peripheries for any language that has undergone standardization. We propose that dialects have a right to be recognized in the language's codified norms because of the impact that standardization has on (peripheral) speakers' perceptions of, and feelings towards, their own varieties. To illustrate these ideas, we use the case of the Catalan language, which has undergone a complex and still incomplete process of standardization since the beginning of the twentieth century. After describing Catalan's current sociolinguistic situation, we analyse the recent Gramàtica de la llengua catalana (2016) by the Institut d'Estudis Catalans (GIEC). The volume approaches linguistic codification as a process of 'prescription through description'.
ДЕВДАРИАНИ Наталья Валерьевна, кандидат философских наук
Introduction<br><br> BOLT English Treebank - SMS/Chat was developed by LDC and consists of English SMS and text chat data with part-of-speech and syntactic structure annotations.<br><br> The DARPA BOLT (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><br> The unannotated English source data is released as BOLT English SMS/Chat (LDC2018T19).<br><br> Part-of-speech and treebank annotation conformed to Penn Treebank II style, incorporating changes to those guidelines that were developed under the GALE (Global Autonomous Language Exploitation) program. Those changes primarily concerned the tokenization of hyphenated words, part-of-speech and tree changes necessitated by the tokenization changes, and updates to the syntactic annotation to comply with updated annotation guidelines. Supplementary guidelines for English treebanks and web text are included with this release. Data<br><br> The source data consists of 115,667 tokens/words in 484 files of English SMS and text chat collected by LDC using two methods: new collection via LDC's collection platform and donation of SMS or chat archives from BOLT collection participants. All of the data was annotated for word-level tokenization, part-of-speech, and syntactic structure.<br><br> Data is presented in a a variety of UTF-8 encoded text formats, specifically, plain text, XML, and Penn Treebank. See the included documentation for more information about specific formats. Acknowledgement<br><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. Samples<br><br> Please view these samples:<br><br> Source (TXT) Penn Treebank (TXT) AG XML<br><br> Updates<br><br> None at this time. Copyright Portions © 2012-2021 Trustees of the University of Pennsylvania
Background: a recurrent linguistic difficulty in the written texts of health professionals lies in the incorrect use of the gerund due to the influence of the English language in Spanish, bad translations, linguistic ignorance and ingrained ―dogmatic― misconceptions. Objective: to base the correct use of the Spanish gerund in scientific writing, as an important and necessary structure of the Spanish language. Methods: a bibliographic review was carried out, 32 texts were consulted ― degree theses, original articles and review articles. After preliminary reading, 20 were selected, taking into account the updating of the bibliography and its relevance to the proposed objective. Results: according to the analysis, the gerund can be correctly used in the most dissimilar situations. The multiplicities of functions granted to this non-personal verbal form by inexperienced in the language have weighed down its functions and have generated fear among those who are unaware of its benefits and possibilities. The authors consider that linguistic improvement in the morphosyntactic knowledge of the language is almost nil in a university context where the priority is the domain of medical sciences, together with the lack of interest of some professionals in the branch for not considering language as the best tool for its intellectual projection and as an inherent aspect of their professional image. Conclusions: it was found that the generality of the consulted authors agree that the gerund can be used in all writing styles, including the scientific one, as long as it is used in correspondence with the linguistic norms.
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.
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.
Purpose and tasks. The purpose is to actualize the linguistic heritage of S. Karavanskyi as a basis for further prescriptive linguistic research. Among the tasks is the analysis of spelling and lexicographic codification in the works of a linguist. The object of our study is the linguistic heritage of Sviatoslav Karavanskyi, who after more than 30 years of Moscow-Stalin concentration camps and 37 years of American emigration carried, preserved and motivated the specific linguistic norm of the constantly destroyed Ukrainian language and its native speakers. The subject of our research is spelling and lexicographic codification of the first third of the XX-XXI century in the works of S. Karavanskyi. When processing the material, we use the analytical and descriptive method. Conclusions and prospects of the study. Spelling issues in the works of S. Karavanskyi have a substantiated ideological basis, which is to reflect the spelling of specific rather than assimilative (“destructive”) features caused by the occupation and totalitarian regime of the 30-80s of the XX century. Spelling assimilation and the necessity to remove it is to change the phonetic-morphological and syntactic structure of the Ukrainian language, in particular phonetic, morphological, word-formation and syntactic changes. The lexicographic codification of the linguist is evidenced by his two fundamental works: “Practical Dictionary of Synonyms of the Ukrainian Language” and “RussianUkrainian Dictionary of Complex Vocabulary”. The main methodological basis for compiling these dictionaries is the specificity of Ukrainian vocabulary in its resistance to codification in dictionaries of “pseudo-language” imposed on Ukrainians during the ethnocide policy and exposing Soviet lexicography as the main “tool of Ukrainian linguicide”. Among the prospects of our study is a holistic linguistic and political portrait of a linguist and socio-political figure.
In this paper, we address the representation of coordinate constructions in\nEnhanced Universal Dependencies (UD), where relevant dependency links are\npropagated from conjunction heads to other conjuncts. English treebanks for\nenhanced UD have been created from gold basic dependencies using a heuristic\nrule-based converter, which propagates only core arguments. With the aim of\ndetermining which set of links should be propagated from a semantic\nperspective, we create a large-scale dataset of manually edited syntax graphs.\nWe identify several systematic errors in the original data, and propose to also\npropagate adjuncts. We observe high inter-annotator agreement for this semantic\nannotation task. Using our new manually verified dataset, we perform the first\nprincipled comparison of rule-based and (partially novel) machine-learning\nbased methods for conjunction propagation for English. We show that learning\npropagation rules is more effective than hand-designing heuristic rules. When\nusing automatic parses, our neural graph-parser based edge predictor\noutperforms the currently predominant pipelinesusing a basic-layer tree parser\nplus converters.\n
Abstract Background: Nowadays, the mobile app market becomes rapidly increased in world wide. The mobile app marketers have smart enough to understand the requirements and demands of customers and perform their aspirations. They delight them. It provides growth, profitability, and creativity with lot of inventions. The main aim of this research is to analyze the customer interest and preferences of mobile service providers. Methodology: This paper proposed the clustering model named as Hierarchical Flexi-Ensemble Clustering (HFEC). It provides the final result with robustness and improved quality. Before clustering, the unwanted features are removed by using the Genetic Algorithm based on the Collective Materials (GACM) technique. The customer preferences are analyzes with the clustering of mobile usage patterns. Results: The analysis determined that the app usage pattern based on the most frequent word, rating category, rating character count, rating word count and content-based rating in the google play store app dataset. Finally, the results are compared with the existing methods to analyze the superior performance of proposed method. The comparison analysis is estimated based on the based on the average hit rate at different cache sizes. Conclusion: The work is concluded with the app pattern prediction in the form of clustering for app marketing service. From the marketing side, they can analyze the customer preferences and satisfaction.
The high memory consumption and computational costs of Recurrent neural network language models (RNNLMs) limit their wider application on resource constrained devices. In recent years, neural network quantization techniques that are capable of producing extremely low-bit compression, for example, binarized RNNLMs, are gaining increasing research interests. Directly training of quantized neural networks is difficult. By formulating quantized RNNLMs training as an optimization problem, this paper presents a novel method to train quantized RNNLMs from scratch using alternating direction methods of multipliers (ADMM). This method can also flexibly adjust the trade-off between the compression rate and model performance using tied low-bit quantization tables. Experiments on two tasks: Penn Treebank (PTB), and Switchboard (SWBD) suggest the proposed ADMM quantization achieved a model size compression factor of up to 31 times over the full precision baseline RNNLMs. Faster convergence of 5 times in model training over the baseline binarized RNNLM quantization was also obtained. Index Terms: Language models, Recurrent neural networks, Quantization, Alternating direction methods of multipliers.
In this paper, we present the results of our experiments concerning the zero-shot crosslingual performance of the PERIN sentence-tograph semantic parser. We applied the PTG model trained using the PERIN parser on a 740k-token Czech newspaper corpus to Hungarian. We evaluated the performance of the parser using the official evaluation tool of the MRP 2020 shared task. The gold standard Hungarian annotation was created by manual correction of the output of the parser following the annotation manual of the tectogrammatical level of the Prague Dependency Treebank. An English model trained on a larger one-million-token English newspaper corpus is also available, however, we found that the Czech model performed significantly better on Hungarian input due to the fact that Hungarian is typologically more similar to Czech than to English. We have found that zero-shot transfer of the PTG meaning representation across typologically not-too-distant languages using a neural parser model based on a multilingual contextual language model followed by a manual correction by linguist experts seems to be a viable annotation scenario.
View of Volume 66, Special Issue, September 2021 The effectiveness of medical evidence is largely dependent on the ability to communicate that evidence to the science-users, mostly patients. Like in many fields of science, also in medicine trust is one of the most important components of doctor-patient interaction. Cultivation of patient trust is, in turn, primarily a linguistic activity, subject to linguistic norms and conventions. Doctor-patient interaction has been at the core of a growing discussion during the past few years, especially in the context of innovations in evidence-based methods and related to the applicability of clinical guidelines derived from those methods. In Italy, this debate resulted in a recent law (n.219/2017), which declares that “the care and trusting relationship between doctor and patient which is based on the informed consent is promoted and enhanced” (art.1) and that “the time of the communication between doctor and patient is a time of care” (art.8). This new kind of perspective on communication between physicians and patients has led to several questions, above all (i) what is the best definition of trust? and (ii) how achieve a trusting relationship? According to a strictly philosophical point of view, it implies how to successfully communicate imperfect evidence and risk to patients who are in a position of epistemic asymmetry with respect to the doctors; it is problematic because it involves a transfer of complex knowledge of risks and uncertainties from experts to laypeople. The paper investigates the difficulties in communicating medical evidence associated with risk and uncertainties of diagnosis and treatment.
Recent advancements in language models based on recurrent neural networks and transformers architecture have achieved state-of-the-art results on a wide range of natural language processing tasks such as pos tagging, named entity recognition, and text classification. However, most of these language models are pre-trained in high resource languages like English, German, Spanish. Multi-lingual language models include Indian languages like Hindi, Telugu, Bengali in their training corpus, but they often fail to represent the linguistic features of these languages as they are not the primary language of the study. We introduce HinFlair, which is a language representation model (contextual string embeddings) pre-trained on a large monolingual Hindi corpus. Experiments were conducted on 6 text classification datasets and a Hindi dependency treebank to analyze the performance of these contextualized string embeddings for the Hindi language. Results show that HinFlair outperforms previous state-of-the-art publicly available pre-trained embeddings for downstream tasks like text classification and pos tagging. Also, HinFlair when combined with FastText embeddings outperforms many transformers-based language models trained particularly for the Hindi language.
Among the various challenges regarding distance education is the necessity of reducing the student dropout rate. In this sense, the present research aimed to contribute to the design of a lexical database focused on emotions and opinions that can be incorporated into a predictive evasion software. For the database design, we used the Scup tool to collect 150 tweets containing distance education students’ opinions and analyzed them in the light of Martin and White’s Appraisal Framework, along with five resources related to the sentiment Analysis field, which were taken from Liu’s work. In addition, we used the Aulete dictionary to describe the lexical units found in our corpus to better fit them into the analysis categories. Results showed 220 opinion tokens, which were identified and labeled according to their polarity. Moreover, these tokens were included in the domains attitude (judgment and appreciation) and graduation (sharp and strong) from the linguistic framework used. The results also indicated the necessity of another resource to help identify the use of figurative language, slangs, and extralinguistic elements, such as GIFS and emojis.