Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
Robert Pugh, Francis Tyers. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.
This paper investigates how linguistic norms are negotiated in German-speaking localities in Rio Grande do Sul, Brazil. The aim of the article is to find out whether linguistic norms still play a role in a heterogeneous multilingual context in which German ceased to be used as a written language while still being transferred to children in spoken varieties. The data is based on 58 semi-structured interviews from ten locations. The analyses take their starting point in language labels, providing key words for identifying and contrasting varieties. The results show that different varieties of German are still clearly perceived and labeled, and that they are evaluated according to the vertical dimension. Norms are negotiated displaying an ideology of linguistic homogeneity and relating to speaker age, the value of written language, and norm instances like schools. Comparing the older and the younger generations, a tendency of norm varieties being less associated with written language and more and more based on surrounding spoken German varieties is perceived, with West Central German (Hunsrückisch) showing some dominance.
Ambiguity is a common phenomenon found across languages and has been studied extensively. Nevertheless, not not much has been done on ambiguity in Eggon. In an attempt to fill the existing gap, the present article studies ambiguity and its intricacies in Eggon Language. Specifically, the research aims at exposing lexical ambiguity in the language, its nature and sources. The study also tries to provide ways of disambiguating such structures. Data is generated through participant observation of native speakers, documented sources (Eggon dictionary) and introspection. Descriptive method is used in analysing the generated data. The findings show that ambiguity is a common phenomenon in Eggon. The use of tone helps to disambiguate some ambiguous words. Moreover, most lexical ambiguities occur due to polysemy and homonymy and can be disambiguated through contextualization. Lexical ambiguity also results in other types of ambiguities in the language, such as semantic and syntactic ambiguities. Further study into dialectal ambiguity will add to Eggon linguistic database.
Abstract Persons with dementia are at a higher risk for circadian disturbances and need more bright light to regulate circadian rhythm due to age-related vision deficiencies and reduced activity in suprachiasmatic nuclei. However, the impact of light exposure on nursing home (NH) residents with dementia has not been well evaluated. This secondary analysis examined the associations of daytime light exposure with affect and neurobehavioral symptoms in this population. Circadian stimulus (CS) was included as a lighting measure. Data of 13-week repeated measures were from a clinical trial with 27 residents with dementia. Participants wore light sensors to measure individual light exposure (lux, CCT, and CS) and Philadelphia Affect Rating Scale and Neuropsychiatric Inventory were used to assess affect and neurobehavioral symptoms every other week. Correlation and multilevel modeling (MLM) analyses were performed. Participants’ average age was 87 and 74% were female. Results showed that higher lux levels were significantly correlated with more contentment (r=0.162, p=0.0399) and less sadness (r=-0.185, p=0.0183), less depression (r=-0.157, p=0.0459), and less anxiety (r=-0.207, p=0.0081). Higher CCT levels were significantly correlated with less delusion (r=-0.182, p=0.0205) and appetite changes (r=-0.159, p=0.0434). Higher CS levels were significantly correlated with aberrant motor behavior (r=-0.171, p=0.0293) and anxiety (r=-0.210, p=0.0072). MLM showed that CS significantly predicted decreased aberrant motor behavior (β=-5.04, p< 0.05), nighttime behavior (β=-4.85, p< 0.05), and anxiety (β=-6.08, p< 0.05). The results revealed the importance of light exposure in this population. The findings can guide environmental design to improve affect and neurobehavioral symptoms for NH residents with dementia.
Background While numerous studies observed notable changes in syntactic complexity among individuals with Alzheimer’s disease (AD), these studies predominantly concentrated on isolated internal structures of language but rarely defined the syntactic complexity variation in AD on a continuum. Given that working memory load exhibits a continuous rather than binary pattern in populations and plays a crucial role in syntactic processing and generation, research on syntactic complexity changes in AD could benefit from expanding to include this perspective.Aims To examine the probabilistic distribution of syntactic complexity in AD under controlled sentence length conditions from the perspective of working memory, and to develop a comprehensive linguistic profile of syntactic complexity variation in AD by analyzing fine-grained syntactic features.Methods & procedures The corpus materials consist of descriptions based on the Cookie-Theft picture component provided by 70 individuals with AD (mean MMSE = 20.97) and 70 cognitively intact elderly individuals (mean MMSE = 29.21) from the DementiaBank clinical language transcript dataset. We employed dependency distance to quantify working memory load and syntactic complexity. Additionally, three finer-grained dependency metrics, namely adjacent dependency distribution (1dd%), mean dependency distance (MDD) and dependency direction distribution, enable a deeper interpretation of the syntactic complexity variations in AD from different perspectives.Results (1) The distribution of dependency distances in AD was similar to that in the healthy control (HC) group, both following the Zipf-Alekseev model, and the variations of the parameters within the model were analogous between the two treebanks; (2) The performance of AD patients in adjacent dependency, MDD and dependency direction differed from that of the HC group across varying sentence lengths; (3) “Simplified” and “ungrammatical” structures were the primary syntactic features in AD.Conclusions These findings confirmed the presence of syntactic impairments in AD. The decline of syntactic complexity can be attributed to the failure of the “End Weight” principle due to working memory deficits in AD patients. The study contributes to the easier identification of AD patients and more effective working memory interventions, thereby improving language production in AD patients.
Individuals with hoarding disorder tendencies can be identified as teenagers and get worse as adults.This study aimed to test the Declutter Challenge intervention in reducing hoarding disorder tendencies using a quasi-experimental approach.Thirty-one teenagers involved in this study screened by the hoarding tendencies screening tool administered online who have hoarding disorder tendencies at moderate to high level.The selection of participants into control and experiment groups was conducted nonrandomized.Data collection methods used observation, interviews, and the Saving Inventory-Revised scale (α = 0.93 and test-retest reliability r = 0.86), Hoarding Rating Scale (α = 0.92), and Clutter Image Rating (α = 0.82).The t-test was used to examine the differences in saving inventory-revised scale scores during the pretest, posttest and follow-up both for experimental and control groups.The results of the experiment group after being treated reported significantly lower levels of hoarding disorder tendencies than the control group ( p< 0.05).There was a significant difference in gained scores between the control group and the experimental group.Therefore, it can be concluded that the Declutter Challenge could help reduce hoarding disorder tendencies.Moreover, a qualitative analysis indicated that several behavioral patterns were found related to hoarding indicators.Besides, the participants felt relieved and happy because they were able to sort and put away items wisely.
Computer lexicography is one of the important directions of modern domestic linguistics and translation studies.Nowadays, scientists face important questions related to the theoretical and practical aspects of compiling computer dictionaries, which, undoubtedly, have significant scientific significance.A necessary stage in solving these questions is to understand the peculiarities of the formation of this section of linguistic science -its preconditions, methodological base; directions of the scientific research.The article is devoted to highlighting some aspects of the historical development of domestic and foreign lexicography.The task of the article is to consider the main stages of the development of computer technologies for compilation of dictionaries and to determine the prerequisites that led to the emergence of such a direction in linguistics as computer lexicography.The advent of computers actively influenced the development of lexicography.Initially, they were used to prepare paper dictionaries, in other words, they served as a typewriter.But later it turned out that computers can perform such functions as editing, storing any lexicographic information, and therefore computer corpora of texts appeared, and then machine-readable dictionaries.emergence of linguistic databases, electronic libraries and card libraries.Automated lexicographic databases in the form of electronic dictionaries are now an integral part of systems of machine translation, information search, editing and correction of texts, as well as processing of large text arrays and their storage as a separate task of creating electronic libraries.Computer dictionaries on optical media enabled translators and scientists to quickly find any information about a word (translation, interpretation, etc.).
Abstract Classical models of tool knowledge and use are centred on dorsal and ventral parietal pathways. Theories of semantic cognition implicate a “hub-and-spoke” network, centred on the anterior temporal lobe (ATL), that underpins all concepts including tools. Despite their prominence, the two theoretical frameworks have never been brought together and the large discrepancy in the functional neuroanatomy addressed. We undertook a multiple-regression Representational Similarity Analysis (RSA) of task fMRI data with four (motor action, broad function, mechanical function, object structure) feature-based models. The motor action model correlated with the activation patterns in bilateral superior parietal lobules (SPL), while the models of broad function and mechanical effect aligned with the activation patterns in bilateral ATLs. The object-structure model correlated with activation patterns in bilateral middle occipital gyri. The results also showed that the ventral ATL activation patterns corresponded simultaneously with all RDM models except object structure. Furthermore, a standard univariate analysis using tool-familiarity ratings for parametric modulation revealed that classical tool-network regions (frontal, inferior parietal, and posterior middle temporal cortices) were increasingly active as the tool familiarity reduced. These results demonstrate that parietal and ATL regions are both crucial and motivate a major extension and revision of the neuroanatomical framework for tool use. Significance Statement This study provides definitive evidence for convergent tool representation in human anterior temporal lobe (ATL), outside the traditionally focused parietal lobe as the critical centre for human tool-use ability. For many years the parietal lobe was considered crucial in recognizing and planning use of familiar objects, while regions in temporal lobe received little attention. Our advanced multi-voxel analysis with artefact-resistive fMRI scanning revealed that both non-motor (tool-function) and motor (kinematics for tool use) information convergently represented in the left ventral ATL, while showing other distributed regions encoding distinct types of tool information in anterior temporal and parietal regions. These findings highlight the ATL’s crucial role in tool representation and necessitate a significant expansion of neuroanatomical framework for human tool-use ability.
<p style="text-align: justify;"><strong>Objective.</strong> This exploratory study investigated whether perspective-taking and awareness of vulnerability procedures could enhance impressions of robots. <br><strong>Background.</strong> A society characterized by the harmonious coexistence of humans and robots is poised for realization in the imminent future. Nevertheless, numerous challenges must be confronted for the materialization of such a societal paradigm. One among them pertains to the prevailing tendency for humans to harbor adverse perceptions of robots, the amelioration of which proves to be a complex endeavor. The present study undertakes an exploratory investigation into strategies aimed at mitigating unfavorable impressions associated with robots. <br><strong>Study design.</strong> Participants were randomly assigned to one of three groups: control group, perspective perception group, and robot vulnerability awareness group, and received different instructions. <br><strong>Participants.</strong> Online experiments were conducted with 360 participants who were asked to imagine and describe a day in the life of a robot, and their impressions of the robot were measured using a questionnaire. <br><strong>Measurements.</strong> Upon conjecturing and articulating the robot's daily routines, participants shared their perceptions of the robot through the application of three assessment tools: the Robot Anxiety Scale, the Mind Attribution Scale, and the Familiarity Rating Scale. <br><strong>Results.</strong> The manipulation checks confirmed successful manipulation, but there was no evidence that perspective-taking or awareness of vulnerability influenced impressions of the robot. <br><strong>Conclusions.</strong> The efficacy of perspective-taking, a technique established as beneficial in ameliorating adverse perceptions of humans, may exhibit diminished effectiveness in the context of alleviating negative impressions associated with robots.</p>
An essential component of finance and investing is stock price prediction, which attempts to project a stock’s future price. The objective is to use a variety of techniques and data sources to predict the direction and size of price changes. Sentiment analysis of financial news data offers insightful information about the state of the market and possible changes in stock prices. Stock price projections become more accurate and dependable when sentiment research is combined with additional machine learning and deep learning models. This research develops a multicollinearity Least Square Recursive Optimised Deep Belief Network Classification (MLSRODBN) method for sentiment analysis-based stock price prediction that promises better accuracy and shorter processing times. The MLSRODBN Method comprises multiple layers for efficient stock price prediction, including preprocessing, feature selection, and classification processes. In hidden layer, Treebank Word Tokenization is performed to partition the sentences into tokens or words. Finally, Partial Least Square Regression Analysis is carried out to perform efficient sentiment classification (i.e., positive, negative, or neutral) based on the extracted keywords from the financial news. The analysis’s conclusions show that the MLSRODBN strategy fared better at predicting stock prices than other deep learning methods that were currently in use.
Currently, Sentiment Analysis (SA) has been gradually applied in a variety of fields and has become one of the most researched topics in adolescent education. However, since the interaction between cognition and emotion is involved in every learning process, it is possible to intervene with students based on the emotions they express in classroom or extracurricular environments, in order to assist teachers in assessing the overall state of students. This is conducive to improving teaching effectiveness, facilitating personalized learning, improving the emotional state and mental health of students, and promoting development and progress in the field of education. Emotion recognition is usually studied using electroencephalography (EEG), which is not practical for the adolescent population that spends most of their time at school almost every day. Therefore, in this paper, we propose an SA method based on a modified transformer network combined with convolutional neural network (CNN), aiming to utilize language for emotion recognition. The experiments were conducted using the Standford Sentinent Treebank (SST) dataset for training and validation of the model, which categorizes emotions into two categories based on positive and negative emotions, and ultimately obtains an overall accuracy of 95.00%. The experimental results demonstrate the recognition ability of our proposed model in sentiment analysis and show the potential for application in adolescent education.
Abstract A characteristic trait of Vedic as well as Classical Sanskrit is the use of nominal compounds. Diachronic linguistic studies have observed an increasing use of compounds in Vedic texts. It is also generally accepted that compounds should be read as syntactic phrases and that they can be equivalent to subordinate clauses. However, it has not been studied so far whether and to which degree compounds replaced competing syntactic structures such as relative clauses or participial constructions over time. Using data from a syntactic treebank of Vedic and early Classical Sanskrit, this paper addresses the questions whether compounding replaced equivalent constructions and which textual and sociolinguistic factors may have driven this process. The paper studies compounds used as adnominal and adverbial modifiers, and compares their frequency distributions with those of subordinate clauses, adjectives, converbs, and participial constructions. Since the number of relevant cases is limited and the sociolinguistic factors driving the use of compounds are not well understood, the observed distributions are modeled with a hierarchical Bayesian framework that extracts an optimal subset from a set of possible explanatory factors (chronology, geography, poetry/prose alternation, genre, and school affiliation of Vedic texts).
The issues of speech etiquette and specific speech expressions frequently become the focus of linguists’ attention, as speech etiquette is an integral part of communicative culture. Studying this phenomenon aids in revealing the communication features characterizing representatives of different nationalities and enables us to understand their mindsets. Furthermore, speech etiquette should be addressed when discussing matters related to the establishment and adherence to linguistic norms. This research focuses on the closely related Turkic languages ~ Tatar and Uzbek. Despite considerable similarities in vocabulary and grammar between these languages, differences exist in the usage of greeting expressions. Linguistic research emphasizes the sociolinguistic and methodological approach to evaluating speech etiquette: it identifies key speech expressions, associated with typical communication situations, and reveals their equivalents in other languages. In both Tatar and Uzbek cultures, the fundamental rules of greeting are largely similar. In this case, interrogative phrases are actively employed.Upon meeting, individuals frequently inquire about each other’s health and daily life, using the phrases “Hcanex, cayavixuei?” (“How is your health?”), “Hcau-cay eoma itopucezme?” (“Are you alive and well?") and the like. People greet one another in various forms and manners, wishing each other good morning, good day, suecess in work, health and well-being. Notably, in Tatar and Uzbek linguocultures, it is customary to inquire not only about the addressee’s health but also about the health and well-being of their relatives and close ones, along with invitations to come and see them
Though the translation of film titles has not been firmly established within translation studies, the existing studies mainly focus on the translation strategies and process analysis from classic theories including Skopos theory and semantic equivalence. Meanwhile, there has been limited research to examine the translation of animation titles. This paper delves into the intricate world of animation titles and their translation dynamics across languages, especially focusing on English, Japanese, and Chinese, employing a methodology that combines data from IMDb.com and chineseanime.org with Weiciyun and Python analysis. The study investigates titles' characteristics and features in each source language and the translation strategies used among them. The findings highlight that English, Japanese, and Chinese animation titles possess distinct structural and semantic attributes. These traits reflect the inherent linguistic norms and cultural preferences of each language. Furthermore, the study uncovers multifaceted translation strategies used to bridge the linguistic gaps among languages, including transliteration, literal translation, and adaptation. As for creative adaptation which has a large percentage of every translation process, it is conspicuous that the characteristics of those translated titles largely remain consistent with the features of the source texts. Also, this study exposes discernible patterns of cultural power dynamics influencing translation strategies, particularly evident in the prevalence of English loanwords and transliterations.
Mastery of basic concepts of the Indonesian language plays a crucial role in supporting the academic and professional success of education students, particularly in the digital era characterized by the rapid development of information and communication technology. The digital era has brought significant changes to students’ language practices, which are indicated by the increasing use of informal language and the neglect of linguistic norms in academic contexts. This study aims to examine the urgency of mastering basic concepts of the Indonesian language for education students in the digital era and its implications for strengthening academic literacy and the professional readiness of prospective teachers. This research employs a qualitative approach using a library research method through the analysis of reference books, scholarly journal articles, and relevant educational policy documents. The findings indicate that mastery of basic Indonesian language concepts serves as a foundation for academic literacy that influences critical thinking skills, argumentative ability, and linguistic accuracy among education students. Furthermore, digital literacy that is not supported by strong conceptual language understanding has the potential to reduce the quality of academic language. Therefore, strengthening the mastery of basic Indonesian language concepts in higher education is a strategic necessity to prepare education students who are adaptive, critical, and professionally competent in the digital era.
Social identities are created from the organisation of a series of coordinates that cross different areas in a community: the characteristics of the social group to which the individual belongs, the position that this individual has within the group, the social attitudes towards the own group and other groups, the type of activity that takes place (public or private), the linguistic policies existing in the community towards the different linguistic norms that coexist in it, etc. By incorporating all these (and other) aspects to the variationist analysis, we are admitting that neither the strictly structuralist nor the strictly interactional positions in Sociolinguistics allow us to properly explain the social dimension of language. The analysis of these relationships allows us to analyse with better criteria the different levels in which the sociocultural meaning that the forms of language acquire in specific social situations is organised. Within the framework of these ideas, this research analyses the way in which six radio broadcasters from the Canary Islands stylise their speech in order to achieve certain communicative purposes.
This study investigates the predicate-argument structure in Korean language processing.Despite the importance of distinguishing mandatory arguments and optional modifiers in sentences, research in this area has been limited.We introduce a dataset with token-level annotations which labels mandatory and optional elements as complements and adjuncts, respectively.Particularly, we reclassify certain Korean phrases, previously misidentified as adverbial phrases, as complements, addressing misuses of the term adjunct in existing Korean treebanks.Utilizing a Korean dependency treebank, we develop an automatic labeling technique for complements and adjuncts.Experiments using the proposed dataset yield satisfying results, demonstrating that the dataset is trainable and reliable.
Abnormal Collocations, a significant aspect of the network Micro-language, challenge the traditional linguistic norms while embodying the creativity and vitality of internet culture. Utilizing a robust semantic knowledge base, this study conducts a systematic analysis of abnormal Collocations in online micro-language, identifying their formation mechanisms and manifestations. Based on this analysis, this paper proposes a targeted approach for the identification and handling of abnormal collocations. The experiments have shown that the construction of relevant knowledge bases and the classification processing of abnormal collocations can significantly improve the accuracy of identifying abnormal collocations. The research offers novel perspectives and insights into the field of network linguistics.
The objective of this study was to create graph embedding vectors using Korean WordNet (KorLex) and apply them to neural network word-embedding models. Semantic knowledge, especially lexical semantic knowledge in a language, can be represented by word-embedding vectors or graph structures of lexical databases, such as WordNet. Both representations capture common semantics; however, some semantic knowledge is only captured in a specific way or not at all. In a previous study, Path2vec mapped WordNet graphs to graph-embedding vectors using similarity scores between two words. In this study, we propose two main approaches. First, we mapped the knowledge in the Korean lexical database KorLex onto graph-embedding vectors. We then applied these embedding vectors to deep neural network word embeddings to capture additional semantic knowledge in the Korean language. On a custom test set, the proposed approach improved performance by capturing additional semantic knowledge in similarity and analogy analyses. We plan to apply a variant of this to other deep neural embedding models.
This article explores the concept of immaginaire linguistique (IL), introduced in the 1970s by Anne-Marie Houdebine, to examine the acceptability judgments of counterfactual conditionals in Italian. Although largely overlooked in Italian sociolinguistics, this concept reflects the social perception of a language within a community and its impact on linguistic norms. The study combines the IL model with previous research on conditionals and the issue of standardization and normativity in historical Italian. Data were collected through an online questionnaire targeting native speakers and Italian teachers. The results reveal significant variability in the acceptance of hypothetical periods as standard, despite their historical attestation in Italian. Analyzed forms include the double imperfect indicative and combinations with the imperfect and pluperfect in the apodosis or protasis. The primary aim is to highlight how codified grammatical models fail to provide precise references for contemporary Italian speakers. The findings suggest that despite a tendency toward linguistic purism, there is a growing openness to grammatical variations. This study emphasizes the need for further research to understand how and why some linguistic norms are not integrated into the IL, demonstrating the persistence of a dichotomous right-wrong view among educators. This conservative approach contributes to the preservation of traditional norms and resistance to non-standard variants.
not-yet-known not-yet-known not-yet-known unknown Emotional experiences involve dynamic multi-sensory perception, yet most EEG research uses unimodal stimuli such as photographs. However, a recent study found that realistic emotional videos reliably reduce the amplitude of a steady state visual evoked potential (ssVEP) elicited by a flickering border. It is unknown how the video-ssVEP measure compares to the well-established Late Positive Potential (LPP) that is reliably larger for emotional scenes. To address this question, 45 participants viewed 90 matched pairs of realistic videos and scenes. Replicating the previous study, emotional videos reduced ssVEP amplitude more than neutral videos. At the group level, the video-ssVEP and scene-LPP measures produced similarly large differences per category (pleasant, neutral, unpleasant), and both measures strongly correlated with arousal ratings. However, trial-based Bayesian multilevel models suggest that the group-level results mask important differences. Consistent with previous research, the scene-LPP was sensitive to specific emotional contents (erotica and gore) more than would be predicted by arousal ratings. In contrast, the video-ssVEP did not show this specific sensitivity, and was better explained by individual arousal ratings collected for each stimulus trial. These results suggest that the 2 measures index partially distinct aspects of emotional perception, with the LPP reflecting the discrimination of emotional features, while the ssVEP indexes emotional engagement. Taken together, the results suggest that a video-ssVEP paradigm has comparable single-trial reliability and may better reflect a more diverse range of experienced emotional states relative to scene-LPP paradigms.
Background: The extent of ischemic injury in acute stroke is assessed in clinical practice using the Acute Stroke Prognosis Early CT Score (ASPECTS) rating system. However, current ASPECTS semi-quantitative topographic scales assess only the middle cerebral artery (MCA) (original ASPECTS) and posterior cerebral (PC-ASPECTS) territories. For treatment decision-making in patients with anterior cerebral artery (ACA) occlusions and internal carotid artery (ICA) occlusions with large ischemic cores, measures of all hemispheric regions are desirable. Methods: In this cohort study, anatomic rating systems were developed for the anterior cerebral (AC-ASPECTS, 3 points) and anterior choroidal artery (ACh-ASPECTS, 1 point) territories. In addition, a total supratentorial hemisphere (H-ASPECTS, 16 points) score was calculated as the sum of the MCA ASPECTS (10 regions), supratentorial PC-ASPECTS (2 regions), AC-ASPECTS (3 regions), and ACh-ASPECTS (1 region). Three raters applied these scales to initial and 24 h CT and MR images in consecutive patients with ischemic stroke (IS) due to ICA, M1-MCA, and ACA occlusions. Results: Imaging ratings were obtained for 96 scans in 50 consecutive patients with age 74.8 (±14.0), 60% female, NIHSS 15.5 (9.25-20), and occlusion locations ICA 34%; M1-MCA 58%; and ACA 8%. Treatments included endovascular thrombectomy +/- thrombolysis in 72%, thrombolysis alone in 8%, and hemicraniectomy in 4%. Among experienced clinicians, inter-rater reliability for AC-, ACh-, and H-ASPECTS scores was substantial (kappa values 0.61-0.80). AC-ASPECTS abnormality was present in 14% of patients, and ACh-ASPECTS abnormality in 2%. Among patients with ACA and ICA occlusions, H-ASPECTS scores compared with original ASPECTS scores were more strongly associated with disability level at discharge, ambulatory status at discharge, discharge destination, and combined inpatient mortality and hospice discharge. Conclusion: AC-ASPECTS, ACh-ASPECTS, and H-ASPECTS expand the scope of acute IS imaging scores and increase correlation with functional outcomes. This additional information may enhance prognostication and decision-making, including endovascular thrombectomy and hemicraniectomy.
In the modern world, where technology and the internet have defined every sphere of our life, traditional communication models have changed and evolved. The growing popularity of the internet and social media has created new, unique forms that often differ from ordinary everyday communication. Electronic discourse is a new form of communication that not only allows users to connect easily and quickly but also creates and develops new linguistic norms. Online communication encompasses diverse platforms, each creating its own unique internet language where users actively employ new linguistic units. In this new reality, among other elements, the significant role of emoji as visual elements becomes apparent. These small symbols help us convey emotions, moods, and context. Emoji fill the emotional vacuum that often accompanies written communication. This article examines the phenomenon of emoji and their impact on modern digital communication. The paper reviews the history of emoji origins, their universal characteristics, and various aspects of their usage. The work discusses both the advantages and disadvantages of using emoji. The central part of the research is dedicated to detailed classification of emoji in YouTube video comments. The analysis of presented Georgian and English examples emphasizes the important role of emoji in online interactions and their ability to enrich online textual communication with emotional nuances. In conclusion, the growing importance of emoji in the digital age and their role in simplifying and diversifying communication is emphasized. Despite the difficulties in understanding emoji and the associated risks of their use, emoji remain a powerful tool in modern online communication, especially on social media platforms.
The expression of an association between a conditioned stimulus (CS) and an unconditioned stimulus (US) can be attenuated by presenting the CS by itself (i.e., extinction, Ext). Though effective, Ext is susceptible to recovery effects such as renewal, spontaneous recovery, and reinstatement. Dunsmoor et al. (2015, 2019) have proposed that pairing the CS with a neutral outcome (novelty-facilitated Ext [NFE]) could offer better protection against recovery effects than Ext. Though NFE has been compared to Ext, it has rarely been compared to counterconditioning (CC), a similar procedure except that the CS is paired with a US having a valence opposite to the US used in initial training. We report two aversive conditioning experiments using the rapid-trial streaming procedure with human participants that compare the efficacies and susceptibilities to ABA renewal of Ext, CC, and NFE. Associative learning was assessed through expectancy learning and evaluative conditioning. CC and NFE equally decreased anticipation of the US in the presence of the CS (i.e., expectancy learning). Depending on how the CS-US association was probed, they were either as or more effective at doing so than Ext. All three interference treatments were equally susceptible to context manipulations. Only CC clearly altered the valence of the CS (i.e., evaluative conditioning). Valence ratings after Ext, CC, and NFE, as well as a no-interference control condition, were all equally susceptible to context effects. Overall, the present study does not support the assertion that NFE is consistently more resistant to recovery effects than Ext. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
FrameNet serves as a comprehensive lexical database intended to represent contemporary language usage. However, it faces challenges in accurately representing specialized domains. Among these domains, FrameNet presents difficulties in capturing the specific semantics of human senses. Senses such as smell and taste are in fact included in more general frames or inadequately represented. Building on a previous resource proposing a new framework for olfactory events, we propose a similar annotation scheme for gustatory references in English, enlightening the potential of frames to effectively capture sensory semantics. Having a comprehensive framework to deal with the annotation of this kind of references in textual data is especially important to develop systems for the automatic extraction of sensory information. Moreover, our approach incorporates words from specific historical periods, thereby enriching the framework’s utility for studying language in a diachronic perspective. In this...
Creativity evaluation, particularly rating the creativity of products, is an unstable construct subjective to the influence of various individual states and characteristics. However, little research has been conducted on whether and how participants’ physiological states affect rating products. This experimental study aimed to explore how synchrony (i.e. whether individuals’ evaluation time synchronizes with their peak circadian arousal periods during a day) affects the rating of creativity in drawings, whether these effects vary according to drawings’ different creativity levels, and the influence of emotional state on synchrony and creativity rating. A sample of 191 university students rated several drawings of low, medium, and high creativity levels at their optimal or non-optimal times of day, and their subjective emotional valence and arousal were recorded. The results showed that participants exhibited a more positive rater bias when rating low- and medium-creativity-level drawings during their optimal times of day. Emotional arousal, but not valence, mediated the relationship between synchrony and rater bias. These findings highlight the role of synchrony and emotional arousal in rating creative products, contributing to our understanding of how raters’ synchrony states influence creativity evaluation.
In this paper, we present the activities and products developed during the Ikpeng language documentation project that contributed to the strengthening of the teaching of mother language of this indigenous society at the school, a space in which the use and teaching of the Portuguese language predominates. The Ikpeng linguistic documentation process was set within the Projeto de Documentação de Línguas Indígenas (PRODOCLIN), promoted by Museu do Índio/FUNAI, in partnership with UNESCO, during the period from 2009 to 2012. Among the main contributions of the documentation project are a sociolinguistic diagnosis, the creation of a lexical database, the elaboration of a descriptive grammar of the Ikpeng language, the publication of a monolingual book of traditional narratives and training courses in linguistics for indigenous teachers. The products resulting from the documentation were developed based on the demands of the school context of the Ikpeng society, to preserve and value the language and ancestral memories of this indigenous society. The Ikpeng language is spoken by the homonymous people, who live in the Parque Indígena do Xingu – MT.
Fear of threatening contexts often generalizes to similar safe contexts, but few studies have investigated how contextual information influences cue generalization. In this study, we explored whether fear responses to cues would generalize more broadly in a threatening compared to a safe context. Forty-seven participants underwent a differential cue-in-context conditioning protocol followed by a generalization test, while we recorded psychophysiological and subjective responses. Two faces appeared on a computer screen in two contexts. One face (CS+) in the threat context (CTX+) was followed by a female scream 80% of the time, while another face (CS-) was not reinforced. No faces were reinforced in the safe context (CTX-). In the generalization test, the CSs and four morphs varying in similarity with the CS+ were presented in both contexts. During acquisition, conditioned responses to the cues were registered for all measures and the differential responding between CS+ and CS- was higher in CTX+ for US-expectancy ratings and skin conductance responses, but the affective ratings and steady-state visual evoked potentials were not context-sensitive. During test, adaptive generalized responses were evident for all measures. Despite increased US-expectancy ratings in CTX+, participants exhibited similar cue generalization in both contexts, suggesting that threatening contexts do not influence cue generalization.
The present research focuses on analyzing the mechanisms of politeness and the role of adjectives in interpersonal relationships, considering them as important tools for expressing attitudes, emotions, and cultural values. Based on the research results, it has been established that politeness strategies and the use of adjectives vary significantly depending on cultural contexts, underscoring the need for a deeper understanding of linguistic norms and variations. Special attention has been paid to the role of adjectives, which serve not only as descriptive elements but also as means of expressing evaluations and emotions, thus, playing a significant role in intercultural interaction. The research conclusions underscore the importance of integrating intercultural understanding into the process of linguistic interaction. It has been revealed that successful intercultural communication requires not only language proficiency but also a deep understanding of cultural differences in politeness strategies. The research also points to the need for further exploration in this field, in particular, in developing practical recommendations for enhancing intercultural communication. In this context, knowledge and application of relevant linguistic strategies can contribute to better understanding and respect among representatives of different cultures.
Natural Language Toolkit (NLTK) is a comprehensive Python library designed to facilitate the exploration, analysis, and processing of human language data. With its extensive collection of tools, NLTK provides researchers, developers, and educators with a powerful platform for tasks ranging from basic text processing to advanced natural language understanding and machine learning. The toolkit includes modules for tokenization, stemming, lemmatization, part-of-speech tagging, named entity recognition, syntactic parsing, semantic analysis, and more. Furthermore, NLTK offers access to numerous linguistic resources such as corpora, lexicons, and treebanks, making it an invaluable resource for both learning and research in the field of natural language processing (NLP). NLTK serves as an indispensable tool for unlocking the complexities of human language
In everyday life, music is increasingly being listened to through headphones and mobile devices in public situations. While a large body of research has demonstrated that music may influence the emotional states of listeners and affect multimodal perceptions i.e. in films, less is known about the music’s impact on environments and the interpretation of social situations. We conducted an online experiment to investigate the influence of music on evaluations, considering individuals’ emotional states (emotion congruence) and group perception. Participants were randomly assigned to one of three experimental conditions (music with positive valence and high arousal, music with negative valence and low arousal, and no music) while viewing images of two different social group types that varied in perceived group characteristics (group members being familiar or unfamiliar with each other). Images were rated on four bipolar scales measuring affective quality and cognitive evaluation of social situations. Results show that individuals who listened to negative music provided lower valence ratings and also judgded social environments lower in terms of pleasantness and cheerfulness (affective) than individuals in the other experimental conditions. In contrast, ratings of crowdedness and familiarity (cognitive) did not differ between experimental conditions. The effect of music on affective evaluations was shaped by social group types, such that participants were more influenced by music when viewing intimacy groups (e.g., friends) than when viewing transitory groups (e.g., strangers). Overall, our results support the assumption of mood congruency for affective evaluations and emphasize the need to consider social information when studying the influence of music on the perception of environments.
Direct dependency parsing of the speech signal -- as opposed to parsing speech transcriptions -- has recently been proposed as a task (Pupier et al. 2022), as a way of incorporating prosodic information in the parsing system and bypassing the limitations of a pipeline approach that would consist of using first an Automatic Speech Recognition (ASR) system and then a syntactic parser. In this article, we report on a set of experiments aiming at assessing the performance of two parsing paradigms (graph-based parsing and sequence labeling based parsing) on speech parsing. We perform this evaluation on a large treebank of spoken French, featuring realistic spontaneous conversations. Our findings show that (i) the graph based approach obtain better results across the board (ii) parsing directly from speech outperforms a pipeline approach, despite having 30% fewer parameters.
Abstract Human creativity originates from brain cortical networks that are specialized in idea generation, processing, and evaluation. The concurrent verbalization of our inner thoughts during the execution of a design task enables the use of dynamic semantic networks as a tool for investigating, evaluating, and monitoring creative thought. The primary advantage of using lexical databases such as WordNet for reproducible information-theoretic quantification of convergence or divergence of design ideas in creative problem solving is the simultaneous handling of both words and meanings, which enables interpretation of the constructed dynamic semantic networks in terms of underlying functionally active brain cortical regions involved in concept comprehension and production. In this study, the quantitative dynamics of semantic measures computed with a moving time window is investigated empirically in the DTRS10 dataset with design review conversations and detected divergent thinking is shown to predict success of design ideas. Thus, dynamic semantic networks present an opportunity for real-time computer-assisted detection of critical events during creative problem solving, with the goal of employing this knowledge to artificially augment human creativity.
This study investigates the potential of large language models (LLMs) to provide accurate estimates of concreteness, valence, and arousal for multi-word expressions. Unlike previous artificial intelligence (AI) methods, LLMs can capture the nuanced meanings of multi-word expressions. We systematically evaluated GPT-4o's ability to predict concreteness, valence, and arousal. In Study 1, GPT-4o showed strong correlations with human concreteness ratings (r =.8) for multi-word expressions. In Study 2, these findings were repeated for valence and arousal ratings of individual words, matching or outperforming previous AI models. Studies 3-5 extended the valence and arousal analysis to multi-word expressions and showed good validity of the LLM-generated estimates for these stimuli as well. To help researchers with stimulus selection, we provide datasets with LLM-generated norms of concreteness, valence, and arousal for 126,397 English single words and 63,680 multi-word expressions.
Social decision-making is known to be influenced by predictive emotions or the perceived reciprocity of partners. However, the connection between emotion, decision-making, and contextual reciprocity remains less understood. Moreover, arguments suggest that emotional experiences within a social context can be better conceptualised as prosocial rather than basic emotions, necessitating the inclusion of two social dimensions: focus, the degree of an emotion's relevance to oneself or others, and dominance, the degree to which one feels in control of an emotion. For better representation, these dimensions should be considered alongside the interoceptive dimensions of valence and arousal. In an ultimatum game involving fair, moderate, and unfair offers, this online study measured the emotions of 476 participants using a multidimensional affective rating scale. Using unsupervised classification algorithms, we identified individual differences in decisions and emotional experiences. Certain individuals exhibited consistent levels of acceptance behaviours and emotions, while reciprocal individuals' acceptance behaviours and emotions followed external reward value structures. Furthermore, individuals with distinct emotional responses to partners exhibited unique economic responses to their emotions, with only the reciprocal group exhibiting sensitivity to dominance prediction errors. The study illustrates a context-specific model capable of subtyping populations engaged in social interaction and exhibiting heterogeneous mental states.
This paper identifies a micro-cue correlating to verb second word order (V2) in two closely related Medieval Romance languages. As V2 is asymmetrically distributed in main rather than subordinate clauses, an asymmetry would be expected in phenomena assumed to relate to V2, such as subject inversion, null subject and enclisis. The loss of that asymmetry should therefore indicate the loss of the V2 word order rule. These assumptions are tested here by a quantitative analysis of a treebank of calibrated data covering the crucial period of change (from the 14th to the 16th century) for Medieval French and Venetian. The hard quantitative evidence provided demonstrates that the main versus embedded asymmetry is indeed a micro-cue of V2 structure, and of its loss in one of the two investigated languages.
Quantization is one of the efficient model compression methods, which represents the network with fixed-point or low-bit numbers. Existing quantization methods address the network quantization by treating it as a single-objective optimization that pursues high accuracy (performance optimization) while keeping the quantization constraint. However, owing to the non-differentiability of the quantization operation, it is challenging to integrate the quantization operation into the network training and achieve optimal parameters. In this paper, a novel multi-objective convex quantization for efficient model compression is proposed. Specifically, the network training is modeled as a multi-objective optimization to find the network with both high precision and low quantization error (actually, these two goals are somewhat contradictory and affect each other). To achieve effective multi-objective optimization, this paper designs a quantization error function that is differentiable and ensures the computation convexity in each period, so as to avoid the non-differentiable back-propagation of the quantization operation. Then, we perform a time-series self-distillation training scheme on the multi-objective optimization framework, which distills its past softened labels and combines the hard targets to guarantee controllable and stable performance convergence during training. At last and more importantly, a new dynamic Lagrangian coefficient adaption is designed to adjust the gradient magnitude of quantization loss and performance loss and balance the two losses during training processing. The proposed method is evaluated on well-known benchmarks: MNIST, CIFAR-10/100, ImageNet, Penn Treebank and Microsoft COCO, and experimental results show that the proposed method achieves outstanding performance compared to existing methods.
Natural language processing for Greek and Latin, inflectional languages with small corpora, requires special techniques.For morphological tagging, transformer models show promising potential, but the best approach to use these models is unclear.For both languages, this paper examines the impact of using morphological lexica, training different model types (a single model with a combined feature tag, multiple models for separate features, and a multi-task model for all features), and adding linguistic constraints.We find that, although simply fine-tuning transformers to predict a monolithic tag may already yield decent results, each of these adaptations can further improve tagging accuracy.1 For example, for each type (unique word form) in the GUM English Universal Dependencies Treebank (see https://universaldependencies.org/) there are 10.7 tokens.For the Latin PROIEL treebank there are only 6.5, and for the Greek Perseus treebank even less, viz.4.8 (note that they are all roughly similar in size: 212K, 205K and 202K tokens respectively).
Background: Despite the frequent comorbidity of affective and addictive disorders, the significance of affective dysregulation in problematic pornography use (PPU) is commonly disregarded. The objective of this study is to investigate whether individuals with PPU demonstrate increased sensitivity to negative emotional stimuli in comparison to healthy controls (HCs). Methods: Electrophysiological responses were captured via event-related potentials (ERPs) from 27 individuals with PPU and 29 HCs. They completed an oddball task involving the presentation of deviant stimuli in the form of highly negative (HN), moderately negative (MN), and neutral images, with a standard stimulus being a neutral kettle image. To evaluate participants' subjective feelings of valence and arousal, the Self-Assessment Manikin (SAM) was employed. Results: Regarding subjective evaluations, individuals with PPU indicated diminished valence ratings for HN images as opposed to HCs. Concerning electrophysiological assessments, those with PPU manifested elevated N2 amplitudes in response to both HN and MN images when contrasted against neutral images. Additionally, PPU participants displayed an intensified P3 response to HN images in contrast to MN images, a distinction not evident within the HCs. Discussion: These outcomes suggest that individuals with PPU exhibited heightened reactivity toward negative stimuli. This increased sensitivity to negative cues could potentially play a role in the propensity of PPU individuals to resort to pornography as a coping mechanism for managing stress regulation.
Conventional continuous emotion prediction systems are typically trained to predict the ‘average’ of affect ratings obtained from multiple human annotators. These systems, however, ignore the ambiguity inherent in the perceived emotions, which is not captured by the ‘average rating’. This paper presents a novel ambiguity-aware continuous emotion prediction system that predicts the time-varying emotion state as a series of beta distributions. Our recent work has shown beta distributions to be an effective parametric model of a collection of affect ratings. This work develops an appropriate cost function that enables neural networks to be trained to predict beta distributions. It also investigates the choice of parameterization of the beta distribution, the choice of activation functions of the output layer, and the tractability of gradient definitions in combination with the loss function. The proposed framework is implemented using a Bag-of-Audio-Words front-end and an LSTM-based back-end and evaluated on the RECOLA dataset. In addition to comparison with baseline systems that only predict the ‘average rating’, the effectiveness with which the predictions represent ambiguity in perceived emotions is also evaluated. Experimental results reveal that the proposed approach outperforms other ambiguity-aware systems, especially when predicting valence.
This chapter describes the courageous pedagogical choices made by the author to design and implement a teaching module entitled, “African American English Ain't Broken: The Linguistic Dexterity of Black Folks” for pre-teacher education students enrolled in an multicultural education class in her university's School of Education. The author describes her intention to focus on Black English as a unique variety of English as an act of resistance to white, hegemonic linguistic norms in teacher education. Grounded in Critical Theory and designed using the principles of Culturally Responsive Teaching, this teaching module enables pre-education students to apply their new knowledge of the nature of Black English to develop an understanding of the systems of power and oppression at work in schools and society that marginalize speakers of African American English, including K-12 students. The chapter concludes with suggestions for ways teacher educators, and K-12 classroom teachers can check their linguistic biases and honor students' home languages in meaningful ways.
Abstract Disabled people encounter numerous barriers to accessibility and face discrimination and inequalities in their daily lives. The situation is even more complex for migrants with a disability, who have to learn how to navigate a new bureaucratic system. This study focuses on deaf adult migrants and the linguistic and bureaucratic challenges they face in Swedish society. The data consists of interviews with 43 deaf migrants participating in language learning courses in four folk high schools catering to deaf people in Sweden. Crip Theory and Crip Linguistics are used as lenses to explore the impact of able-bodiedness and linguistic norms on this particular group. The findings show that deaf migrants experience infantilisation, that sign language interpreters are often seen as a one-size-fits-all solution without much consideration for other factors influencing communication, and that normative able-bodiedness underlies many of the bureaucratic issues deaf migrants face.
Pavlovian fear conditioning and extinction represent learning mechanisms underlying exposure-based interventions. While increasing evidence indicates a pivotal role of disgust in the development of contamination-based obsessive-compulsive disorder (C-OCD), dysregulations in conditioned disgust acquisition and maintenance, in particular driven by higher-order conceptual processes, have not been examined. Here, we address this gap by exposing individuals with high (HCC, n = 41) or low (LCC, n = 41) contamination concern to a conceptual-level disgust conditioning and extinction paradigm. Conditioned stimuli (CS+) were images from one conceptual category partially reinforced by unconditioned disgust-eliciting stimuli (US), while images from another category served as non-reinforced conditioned stimuli (CS-). Skin conductance responses (SCRs), US expectancy and CS valence ratings served as primary outcomes to quantify conditioned disgust responses. Relative to LCC, HCC individuals exhibited increased US expectancy and CS+ disgust experience, but comparable SCR levels following disgust acquisition. Despite a decrease in conditioned responses from the acquisition phase to the extinction phase, both groups did not fully extinguish the learned disgust. Importantly, the extinction resilience of acquired disgust was more pronounced in HCC individuals. Together, our findings suggest that individuals with high self-reported contamination concern exhibit increased disgust acquisition and resistance to extinction. The findings provide preliminary evidence on how dysregulated disgust learning mechanism across semantically related concepts may contribute to C-OCD.
Background/Objective: The multidimensional model of the subjective orgasm experience has been validated only in \nthe sexual relationship context, with no evidence for its validity in the solitary masturbation context. This study aims \nto provide validity evidence for this model in the solitary masturbation context by examining the association of its \ndimensions (affective, sensory, intimacy, and rewards) with different sexual arousal measures. Method: Thirty men \nand thirty women viewed content-neutral and sexually explicit masturbation films. Subjective orgasm experience, \npropensity for sexual excitation/inhibition, rating of sexual arousal, rating of genital sensations and genital response \n(penile erection or vaginal pulse amplitude) were assessed. Regression models were conducted to explain the subjective \norgasm experience from sexual arousal measures. Results: Propensity for sexual excitation, propensity for sexual \ninhibition, and the rating of sexual arousal was associated with the different dimensions of the orgasm experience in \nmen, while in women, the rating of sexual arousal and the rating of genital sensations was associated with the sensory \ndimension. Conclusions: Validity evidence is provided for the multidimensional model of the subjective orgasm \nexperience in the solitary masturbation context.
Prosocial and moral behaviors have overlapping neural systems and can both be affected in a number of psychiatric disorders, although whether they involve similar neurochemical systems is unclear. In the current registered randomized placebo-controlled trial on 180 adult male and female subjects, we investigated the effects of intranasal administration of oxytocin and vasopressin, which play key roles in influencing social behavior, on moral emotion ratings for situations involving harming others and on judgments of moral dilemmas where others are harmed for a greater good. Oxytocin, but not vasopressin, enhanced feelings of guilt and shame for intentional but not accidental harm and reduced endorsement of intentionally harming others to achieve a greater good. Neither peptide influenced arousal ratings for the scenarios. Effects of oxytocin on guilt and shame were strongest in individuals scoring lower on the personal distress subscale of trait empathy. Overall, findings demonstrate for the first time that oxytocin, but not vasopressin, promotes enhanced feelings of guilt and shame and unwillingness to harm others irrespective of the consequences. This may reflect associations between oxytocin and empathy and vasopressin with aggression and suggests that oxytocin may have greater therapeutic potential for disorders with atypical social and moral behavior.
Old Permic, also known as Old Komi, is an extinct variety of Komi that was spoken in the late Middle Ages in the lower Vychegda river basin in Northeastern European Russia, in an area that currently is not Komi-speaking. This language variety is attested in fragmentary records from the 14th to 17th century written both in the Old Permic alphabet and in Cyrillic. These records are of significant importance for research on the history of the Komi language. Here we introduce our attempt towards a new Universal Dependencies treebank that will eventually contain the existing corpus of Old Permic in a structured and CoNLL-U annotated format. This will be the first time this material is being made openly available in digital format, and our contribution describes the current state of the art and remaining challenges.
Статтю присвячено розкриттю військових псевдонімів як одного із показників міжособистісної комунікації військовослужбовців у період російсько-української війни 2014–2023 рр. Завдання дослідження – схарактеризувати лексичну базу цих одиниць, розкривши передумови її розвитку та порівнявши з лексичною базою позивних противника. Застосована для аналізу теорія становить взаємодію положень про системність лексики та мовної діяльності, про сутність і функції неофіційного імені на війні у зв’язку зі змістом соціолінгвістичних категорій «неофіційне ім’я», «сленг», «соціогрупа». База даних включає 500 позивних українських учасників російсько-української війни, 500 псевд із минулого століття (для встановлення передумов розвитку лексичної бази сучасних неофіційних імен) та 500 неофіційних імен ворога (для порівняння лексичних баз неофіційних імен українських військовослужбовців і противника). Зіставний та біографічний методи аналізу дозволили отримати нові результати про динаміку лексичної бази позивних у середовищі представників професійно-соціальної групи «українські військовослужбовці». Застосований якісний підхід до лексикологічного аналізу проблеми сприяє формуванню теорії інтерактивної соціолінгвістики.
Abstract The paper analyses the correlation of change in word concreteness ratings with semantic change. To perform the analysis, we apply a neural network to diachronic data to obtain concreteness ratings of English words. As input to the model, we use co-occurrence statistics with the most frequent words extracted from the Google Books Ngram diachronic corpus. It is shown that the model, initially trained on data averaged over a long time interval, predicts the concreteness ratings with high accuracy (based on the word co-occurrence data in a particular year). The impact of lexical semantic change on the change in the concreteness rating is analyzed using 69 words borrowed from previous works. As the considered cases show, the neural network estimate of the word concreteness rating is very sensitive to changes in semantics. Among the factors that influence changes in the concreteness rating, we reveal the emergence of new meanings of a word, the competition of word meanings related to different parts of speech, the use of a word as a proper name, and the use of the word as a part of collocations. It is shown in the paper that changes in the concreteness rating can (along with changes in other word properties) serve as a marker of semantic change.