Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
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.
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
Kljub porastu jezikoslovnih raziskav govorjene slovenščine, ki si prizadevajo za popis številnih doslej prezrtih posebnosti govorjenega jezika v primerjavi s pisnim, metodologija tovrstnih razprav večinoma temelji na kvalitativni analizi razmeroma majhnih ter zvrstno ali demografsko omejenih vzorcev jezikovne rabe, kar omejuje ponovljivost raziskav in možnost posploševanja spoznanj na govorjeno slovenščino kot celoto. Kot eno izmed možnosti za premostitev tega problema v prispevku predstavljamo drevesnico govorjene slovenščine SST (angl. Spoken Slovenian Treebank), prostodostopni oblikoslovno in skladenjsko označeni reprezentativni vzorec referenčnega korpusa govorjene slovenščine Gos, in ponazarjamo njen metodološki potencial za nadaljnje korpusne raziskave govorjene slovenščine. Na primeru treh tipično govorjenih pojavov (samopopravljanja, diskurzni členki in dodani ujemalni pridevniški prilastki) prikažemo uporabo drevesnice SST za enostaven priklic številnih avtentičnih primerov rabe, na primeru analize pogostosti samopopravljanj glede na različne sporazumevalne okoliščine pa ponazorimo tudi njeno uporabnost za raznolike statistične analize jezikovne rabe. Poleg najpomembnejših prednosti drevesnice SST, kot so uravnoteženost, odprta dostopnost, ročna slovnična označenost in neposredna primerljivost z drugimi tovrstnimi korpusi po svetu, v sklepnem delu izpostavimo tudi nekaj omejitev, kot sta razmeroma majhna velikost ter robustna, v pisni jezik usmerjena označevalna shema.
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.
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).
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.
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.
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.
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.
Emotion recognition is significantly enhanced by integrating multimodal biosignals and IMU data from multiple domains. In this paper, we introduce a novel multi-scale attention-based LSTM architecture, combined with Squeeze-and-Excitation (SE) blocks, by leveraging multi-domain signals from the head (Meta Quest Pro VR headset), trunk (Equivital Vest), and peripheral (Empatica Embrace Plus) during affect elicitation via visual stimuli. Signals from 23 participants were recorded, alongside self-assessed valence and arousal ratings after each stimulus. LSTM layers extract features from each modality, while multi-scale attention captures fine-grained temporal dependencies, and SE blocks recalibrate feature importance prior to classification. We assess which domain's signals carry the most distinctive emotional information during VR experiences, identifying key biosignals contributing to emotion detection. The proposed architecture, validated in a user study, demonstrates superior performance in classifying valance and arousal level (high / low), showcasing the efficacy of multi-domain and multi-modal fusion with biosignals (e.g., TEMP, EDA) with IMU data (e.g., accelerometer) for emotion recognition in real-world applications.
To measure emotion in daily life, studies often prompt participants to repeatedly rate their feelings on a set of prespecified terms. This approach has yielded key findings in the psychological literature yet may not represent how people typically describe their experiences. We used an alternative approach, in which participants labeled their current emotion with at least one word of their choosing. In an initial study, estimates of label positivity recapitulated momentary valence ratings and were associated with self-reported mental health. The number of unique emotion words used over time was related to the balance and spread of emotions endorsed in an end-of-day rating task, but not to other measures of emotional functioning. A second study tested and replicated a subset of these findings. Considering the variety and richness of participant responses, a free-label approach appears to be a viable as well as compelling means of studying emotion in everyday life.
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.
Abstract The exact nature of French liaison as a phonological or morphological alternation is still debated. Under the phonological analysis, liaison is allophony: liaison consonants are special phonemes that alternate between a consonant allophone and zero (e.g., [t] ∼ ∅), the zero allophone being derived from the consonant phoneme through deletion (/t/ → ∅). Under the morphological analysis, liaison is allomorphy: liaison words have two underlyingly listed allomorphs, a consonant-final allomorph and a shorter allomorph that lacks this consonant (e.g., grand ‘great’ /gʁɑ̃t, gʁɑ̃/). This paper uses evidence from lexical statistics to arbitrate between these two analyses. The form without liaison consonant (and with deletion, under the phonological analysis) has been found in previous research to become less likely with increasing lexical frequency. The paper shows that this is problematic for the phonological analysis of French liaison, as deletion typically applies more frequently in high-frequency words across languages. The paper further shows, using evidence from a large lexical database, that words involved in liaison alternations generally have lower type frequency but higher token frequency than non-liaison words when phonotactic and morphological effects on lexical frequency are controlled for. This result is in line with the predictions of the morphological analysis, as allomorphy typically involves a relatively small number of words that occur frequently. Due to its empirical nature, this argument constitutes to date one of the strongest arguments in favor of the morphological analysis.
The development of formal models of decision making under risk has been shaped largelyby decisions between options with monetary outcomes. The most prominentmodel—cumulative prospect theory (CPT)—is good at describing choices betweenmonetary lotteries, but performs less well with nonmonetary and nonnumerical outcomes(e.g., medications with possible side effects). We suggest that affective processes, which arenot considered in CPT, play a larger role in nonmonetary than in monetary choices, andpropose two psychologically motivated modifications to CPT’s modeling framework tocapture these differences: (a) using affect ratings rather than monetary equivalents torepresent the subjective value of nonmonetary outcomes (affective valuation); and (b)allowing the probability weighting of an outcome to depend on the amount of affecttriggered in a choice problem (affective probability weighting). We compared model variantsof CPT implementing the proposed modifications in four empirical datasets (totalN = 240). For choices between options with negative nonmonetary outcomes (medicationswith possible side effects), these modifications substantially improved model performancerelative to the standard implementation of CPT. The same did not hold for monetarychoices. Further, an eye-tracking study on nonmonetary choice (N = 68) provided evidencefor two key behavioral and cognitive predictions of affective probability weighting—namely,that risk aversion increases and attention to probability information decreases as theaffective value of the worst outcome in a choice problem increases. Our work integratesprevious ideas on how affect guides and modulates preference construction within acomputational model and delineates an important context in which these mechanismsapply.
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.
Social mediator robots have shown potential in facilitating human interactions by improving communication, fostering relationships, providing support, and promoting inclusivity. However, for these robots to effectively shape human interactions, they must understand the intricacies of interpersonal dynamics. This necessitates models of human understanding that capture interpersonal states and the relational affect arising from interactions. Traditional affect recognition methods, primarily focus on individual affect, and may fall short in capturing interpersonal dynamics crucial for social mediation. To address this gap, we propose a multimodal, multi-perspective model of relational affect, utilizing a conversational dataset collected in uncontrolled settings. Our model extracts features from audiovisual data to capture affective behaviors indicative of relational affect. By considering the interpersonal perspectives of both interactants, our model predicts relational affect, enabling real-time understanding of evolving interpersonal dynamics. We discuss our model's utility for social mediation applications and compare it with existing approaches, highlighting its advantages for real-world applicability. Despite the complexity of human interactions and subjective nature of affect ratings, our model demonstrates early capabilities to enable proactive intervention in negative interactions, enhancing neutral exchanges, and respecting positive dialogues. We discuss implications for real-world deployment and highlight the limitations of current work. Our work represents a step towards developing computational models of relational affect tailored for real-world social mediation, offering insights into effective mediation strategies for social mediator robots.
The traditional ontological division between the lexicon and grammar has often resulted in a reductionist view of the lexicon and lexical competence as composed of individual words in isolation. This narrow view of the lexicon has transcended disciplinary boundaries and is apparent in the focus on single words of widely used psycholinguistic measures of language (e.g. picture naming, verbal fluency, lexical decision) and cognitive ability alike (e.g. Stroop, recall in working memory span). This article argues that such an approach has imposed limitations on the questions that can be examined by researchers and on the generalizability of some results. Here I propose that, rather than assigning multiword units their own ‘niche’ in psycholinguistic studies, they should be viewed as part of the way core lexical competence is measured and conceptualized, both in monolingual and bilingual speakers. I also review promising advances in recent years brought about by a surge in the number of studies focused on multiword units and by new tasks of individual cognitive skill (e.g. multiword-based chunking ability). These hold the potential to allow for a cross-disciplinary shift in the examination of lexical competence as grounded in community-based norms and in line with current usage-based approaches.
The paper focuses on the problem of language ecology in modern academic texts and analyses Russianlanguage scientific articles on construction and building. The study is relevant due to the need to establish linguoecological norms to improve the effectiveness of scientific communication. The study aims to identify the main linguistic and ecological problems of scientific text, i.e. lexical, grammatical, style errors, excessive nominalization, and syntactic complexity. The material of the study includes Russian-language scientific articles of the thematic area “Construction and Architecture” published in the journal “Bulletin of SUSU” in 2023. Linguotoxic elements can be traced both in the title complex, in the abstracts and full texts of research papers. The most characteristic linguoecological problems of scientific texts of this area are syntactic complexity, excessive nominalization, as well as cliches. The formation of linguoecological competence of a researcher will improve academic literacy and develop the culture of academic writing.
The global dominance of English has transformed it into a dynamic, hybrid language enriched by contributions from non-native speakers. This paper investigates the integration of loanwords, grammatical structures, and narrative techniques from languages such as Spanish, French, Arabic, and Kurdish into English, emphasizing the role of non-native authors in reshaping its lexicon and syntax. Through a mixed-methods approach—combining corpus analysis, lexical examination, and discourse studies—the study reveals how linguistic borrowing reflects sociocultural exchanges and challenges traditional norms of "standard" English. Case studies of authors like Khaled Hosseini and Sheni A. Othman illustrate how multilingual narratives preserve cultural identity while innovating English literary expression. Findings indicate that loanwords often retain phonological and semantic traits of their source languages, with social media accelerating their adoption. Grammatical adaptations, such as syntactic calques and code-switching, further demonstrate the fluidity of English in multicultural contexts. The paper argues that non-native contributions foster linguistic diversity, though tensions persist between global intelligibility and local authenticity. By examining these phenomena, the study advocates for inclusive language policies that recognize non-native varieties as legitimate forms of English. Ultimately, this research underscores the transformative power of linguistic hybridity, positioning English as a living, evolving entity shaped by its global users rather than a static, monolithic system.
OBJECTIVE: Aim: Studying of psycholinguistic features of doctors' communication competence in Ukraine under war conditions. PATIENTS AND METHODS: Materials and Methods: Bibliosemantic method; method of system analysis, comparison and generalization; empirical methods - direct observation of the doctors' and patients' living language, typology of empirical data according to socio-demographic indicators. RESULTS: Results: Within the study, 286 dialogues were collected. With voluntary consent, they were recorded in video and audio formats in compliance with ethical, bioethical, and legal norms. Next, initial typology of dialogues, their lexical and semantic analysis with identification of typical positive and negative communicative strategies were carried out. With the help of the ≪Textanz≫ specialized computer software, 48 dialogues were subjected to the content analysis procedure for two separate ≪Doctors≫ and ≪Patients≫ samples. CONCLUSION: Conclusions: The results of the analysis of ≪Doctor-Patient≫ dialogues enabled identifying and describing psycholinguistic markers of typical physiological, mental, social, and spiritual states of individuals seeking medical help under martial law. Thus, the markers of positive emotional states (optimism, confidence, empathy, etc.) and affective, negative emotional processes (anxiety, fear, anger, aggression, sadness, depression, etc.) were identified.
The lexical and phraseological level of the language system is constantly in dynamics, reflecting the communicative needs of society. Changes in the socio-political life of recent decades have also caused a change in the attitude of native speakers of modern Russian to the language norm. The boundary between codified and uncodified speech is not always clear. This determines the active interaction of colloquial and slang speech. The increased expressiveness of oral speech causes people who use urban slang to need to transform the linguistic means they know, entailing their reduction and increasing the expressiveness of the utterance. Such processes contribute to the use of phraseological units as a means of expressive derivation. However, a free transition from communication involving slang units to communication within the framework of literary and colloquial speech can ensure that some of the reduced phraseological neologisms enter the circle of colloquial units, and then it is possible to continue the derivation process, as a result of which the language system can be enriched with a new lexical unit. Using the example of the phraseological neologism “na krainyak” (to the extreme), one can see the mechanism of action of phraseological reduction as an intermediate stage of expressive derivation.
This paper introduces the Cosine-Gated Long Short-Term Memory (CGLSTM), a novel architecture that integrates a cosine similarity-based gate with the vailla LSTM framework to improve sequence prediction accuracy. It addresses long-term dependencies in sequence data. Through experiments across multiple datasets and tasks such as the adding problem, MNIST and Fashion-MNIST classification, IMDB sentiment analysis, and language modelling on the Penn Treebank, the CGLSTM's performance is evaluated against LSTM, Gated recurrent unit, Recurrent Attention Unit, and Transformer models. Additionally, its effectiveness is demonstrated in the SocNavGym environment, highlighting its potential for real-world applications. Results show the CGLSTM model's superior capability in complex sequences, providing evidence that the integration of cosine similarity into LSTM leads to a 17% improvement in predictive accuracy and efficiency, offering a promising solution for various deep learning applications.
The article analyzes expressive ways of creating new lexical units. The material consists of contexts of various contents extracted from the open resources of the Telegram messenger in the space of 2021–2023. Research methods and techniques include continuous sampling, the descriptive method with elements of interpretive analysis, word-formation analysis. New nominations formed with the help of contamination have been identified. Such word-forming neologisms are used to create a comic effect. The contaminated innovations that participate in the strategy of discrediting Orthodoxy are analyzed. New lexical units indicate a low communicative competence of the addresser. Tmesis nominations are characterized. It is proved that these media innovations have a special expressiveness, attract the user’s attention. The new nominations that have arisen as a result of the inter-word overlap are considered. It is confirmed that media innovations created with the help of inter-word overlay express evaluation and are a means of language play. The new nominations formed as a result of graphixation and its varieties are analyzed. Graphic hybrids participate in the creation of intentional ambiguity, express an individual element, stimulate the imagination of readers. New lexical units based on precedent phenomena are registered. It is established that such new nominations attract the attention of the addressee, disrupt predictability, enhance the expressiveness of the title. Such media innovations are noted, which have expletive and obscene lexemes in their structures as derivational bases. Aggressive nominations cause users to backlash, to violate ethical norms of speech behavior. The results of this paper may be of interest to teachers, students, cadets, as well as to anyone interested in active processes in the field of word formation.
The article analyzes the artistic text of Leo Tolstoy's first work "Childhood" in a syn-chronic aspect using the methods of computational linguistics. The paper shows how digital technolo-gies can be used to establish neologic formations, while observing a picture of the development of nineteenth-century literary language, the formation of norms, and the expansion of the range of grammatical and lexical tools of the language. Methodologically, the study is based on the “superimposition” of a set of unique words from the text of the story “Childhood” on the reference text closest to the time of its creation – the Dictionary of the Living Great Russian Language by V. I. Dahl. The study provides a detailed justification for this pos-sibility. A Python program is used to automatically create lists of unique words from the dictionary and the work. The paper describes the algorithm of the program including: text preprocessing, tokenization, lemmatization, and manual verification. The comparative results revealed 134 unique entities that are neologic phenomena. The authors have carried out their manual verification using the National Corpus of the Russian Language, differen-tial vocabulary of the Dictionary of the Russian Language of the 19th Century, and the Sociolit digital platform. The article analyzes the lexical neoplasms of L. N. Tolstoy, interprets occasionalisms, words used by Tolstoy for the first time in the history of the Russian literary language of the new time, 2 hapaxes of “Childhood”, as well as a word with a new meaning developed in the language of that time and fixed. In this way the authors conduct a study of the writer's contribution to describing the picture of the vo-cabulary formation of the Russian literary language, reflecting the changes in the spiritual, social and material life of Russian society in the middle of the nineteenth century.
Social power can activate behavior toward goal attainment. In the context of romantic and sexual relationships, social power may facilitate competitor derogation tactics and self-promotion tactics to attract a partner. We hypothesized that perceived invulnerability to harm would provide a pathway linking social power to competitor derogation, whereas self-perceived mate value would provide a pathway linking social power to self-promotion. Findings from 218 participants (Mage = 38 years) revealed that experimentally manipulated social power enhanced perceived invulnerability, which in turn was positively associated with competitor derogation. Social power did not affect ratings of self-perceived mate value. Women more strongly endorsed self-promotion in pursuit of a short-term (vs. long-term) relationship, whereas men’s ratings did not vary by relationship goal. Our findings suggested that social power may influence goal-directed thinking and behavior in the context of romantic and sexual relationships.
A Dictionary of Old Norse Prose (ONP) is a lexicographic project that describes the vocabulary of the medieval language of Iceland and Norway. ONP has a complex history, transitioning from a traditional citation collection to a partial print publication and now existing as an online digital resource. The chapter is a case study that aims to identify and explore the essential components involved in establishing, developing, and advancing the dictionary over time. It also aims to examine the process of collecting lexicographic data, organizing them within a lexical database, and adapting the database to meet the project’s evolving needs. The study showcases how the database has expanded and transformed, becoming the fundamental component of the digital dictionary and playing a central role in the current online platform’s practical implementation and functionality. Ultimately, the study shows how a historical dictionary project has adapted to technological advancements and demonstrates multiple ways of using, enhancing and presenting various data that have been collected over a long period of time.
Taking into account the comparative-legal method, the article highlights the legal basis for the transfer of criminal prosecution (legal proceedings) of foreign states, belonging to the Romano-Germanic (continental law systems), Anglo-Saxon legal family (common law systems), as well as mixed type. Due to the fact that the effectiveness of this type of international cooperation in the field of criminal proceedings directly depends on the elaboration of the norms of national law and their compliance with international legal regulations, the author paid considerable attention to the legal regulation of those states with which the competent authorities of the Russian Federation had relations in the field of criminal Justice. The analysis used materialistic dialectics, legal hermeneutics (legal exegesis), special legal, comparative legal methods, sociological and linguistic approaches (component analysis of lexical meanings and analysis of translation transformations), as well as the forecasting method. The theoretical basis for the study was the work of both domestic and foreign lawyers, and among the regulatory framework, both international documents and national legislation of the Russian Federation and a number of foreign countries were highlighted. It is also important to use as an empirical basis for the study the materials of some criminal cases in which the intersystem institution of transfer of criminal prosecution (legal proceedings) was involved. Based on the results of the study, appropriate conclusions were drawn that have both theoretical and applied purposes. In particular, the author believes that the national legislation of many foreign countries contains provisions that in one way or another affect the transfer of criminal prosecution (legal proceedings). Some sovereign governments regulate this institution in general terms and relate it to the scope of providing legal assistance in criminal cases, others, on the contrary, detail it and distinguish it along with other types of international cooperation in the field of criminal proceedings (extradition, legal assistance in criminal cases, recognition and execution decisions of foreign judicial authorities), which we believe is justified. Therefore, it seems necessary to take into account the legislative experience of foreign colleagues, as a result of which we note the urgent procedural need for the adoption of new and additions to the existing domestic criminal procedural norms governing the issues of sending and accepting criminal case materials to/from a foreign state (a) in order to optimize this area of international activity of the Russian Federation, the national legislation of many foreign countries contains provisions that in one way or another affect the transfer of criminal prosecution (legal proceedings).
Abstract Semantic representation is the task of conveying the meaning of a natural language utterance by converting it to a logical form that can be processed and understood by machines. It is utilised by many applications in natural language processing (NLP), particularly in tasks relevant to natural language understanding (NLU). Due to the widespread use of semantic parsing in NLP, many semantic representation schemes with different forms have been proposed; Universal Conceptual Cognitive Annotation (UCCA) is one of them. UCCA is a cross-lingual semantic annotation framework that allows easy annotation without requiring substantial linguistic knowledge. UCCA-annotated datasets have been released so far for English, French, German, Russian, and Hebrew. In this paper, we present a UCCA-annotated Turkish dataset of 400 sentences that are obtained from the METU-Sabanci Turkish Treebank. We provide the UCCA annotation specifications defined for the Turkish language so that it can be extended further. We followed a semi-automatic annotation approach, where an external semantic parser is utilised for the initial annotation of the dataset, which is manually revised by two annotators. We used the same semantic parser model to evaluate the dataset with zero-shot and few-shot learning, demonstrating that even a small sample set from the target language in the training data has a notable impact on the performance of the parser (15.6% and 2.5% gain over zero-shot for labelled and unlabelled results, respectively).
Abstract: The purpose of the current study was to develop a database of gaming photo stimuli to be used in future psychological research assessing behavioral, cognitive, and neural correlates related to gaming. Participants (ages 18-42, N = 549; 43.17% male) completed ratings on 119 gaming-related images across 5 different categories: valence, arousal, relevance, urge, and interest. A measure of gaming addiction was also included. Positive associations between gaming addiction scores and image ratings were predicted. Gamers rated images higher than non-gamers across multiple dimensions including valence (p =.0012), arousal (p <.0001), urge (p <.0001), and interest (p <.0001). Gaming addiction scores were positively associated with image ratings for valence, r =.399, arousal, r =.438, relevance, r =.215, urge, r =.550, and interest, r =.523, p <.0001. Finally, average image ratings for the overall sample ranged from 5.65 (SD = 2.04) to 3.63 (SD = 1.91) for relevance and interest, respectively. These findings suggest that databases of video gaming imagery, rated for valence, arousal, relevance, urge, and interest, could possibly be used in studies assessing cognitive processing of video gaming-related stimuli in individuals with problematic gaming behavior.
Abstract Purpose Previous discussions have characterized hookup culture as ambiguous by nature, but social psychological theory tells us people dislike ambiguity in practice. Meanwhile, a myriad of undefined relationship terms (e.g., talking to, hanging out, having a thing) arose and have remained in use. I examine (1) whether these different “situationship” labels have distinct affective meaning and (2) what that suggests for those occupying the concomitant identities (i.e., assess the behavioral and emotional consequences of being “someone in a _____ relationship”). Approach Using affect control theory and a sample of young adults in defined (N = 50) and undefined (N = 43) relationship types, I test if affective ratings of various relationship label identities are statistically distinct. I then computationally model social events with each relationship label as actor (X identity performs [behavior]), compare their differing levels of social discomfort, and empirically predict the emotions each identity would feel. Findings Undefined relationship labels are not synonymous. Correspondingly, the nature, emotions, and expected behaviors of the individuals with those labels' related relational identities are not equivalent. In cultural evaluation, all undefined relationship labels are lower than all defined relationship labels. In event simulations, predicted deflection levels and actor consequent emotions (how normative is it and how jarring does it feel) were patterned by the labels' cultural evaluation ratings, these correlate with relationship commitment level. Implications By interpersonal necessity, individuals make fine distinctions in shared meanings within a cultural context of constant redefinition. Physically and emotionally negative behaviors are culturally more expected and accepted in undefined contexts by the culturally-understood nature of – and shared perspectives of participants concerning – those relationships’ parameters.
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.
The research aims to identify the characteristics of Kate Middleton’s speech manner and communication style as a representative of the linguocultural type “lady” in British linguoculture. The scientific novelty of the work lies in revealing the dynamism and variability of the linguocultural type “lady” through a complex analysis of Kate Middleton’s speech portrait in the context of modern cultural realities. The research is the first one to integrate phonetic, grammatical, and lexical aspects of speech, which contributes to a more complete understanding of how speech characteristics shape public image and reflect cultural expectations. The article examines interviews and public speeches by Kate Middleton, which makes it possible to analyze the characteristics of her speech in various contexts. As a result, key aspects of Kate Middleton’s speech portrait as a representative of the “lady” type have been identified. The strict adherence to Received Pronunciation (RP) norms, expressed in the clear articulation of consonants and adherence to rhythm, emphasizes Kate Middleton’s commitment to the traditions of aristocratic communication. The active use of abstract vocabulary and evaluative adjectives creates emotionally rich communication, contributing to the formation of trusting relationships with the audience. The use of the imperative mood and personal pronouns highlights Kate Middleton’s leadership qualities and strategic focus on establishing close contact with interlocutors. These results confirm that Kate Middleton is a striking example of the combination of traditional refinement and modern communication norms in the context of the linguocultural space.
Using non-words in psycholinguistic research allows for a high level of control over experimental stimuli.However, this relies on the assumption that they reflect natural language.Eliciting acceptability judgements from L1 speakers of the target language is one approach to ensuring the relative authenticity of non-words.For tonal languages, it is as yet unclear whether tone interacts with the perceived acceptability of non-words.In this between-participant Mandarin non-word norming study, 72 L1 Mandarin listeners judged 750 syllables across five tones: tones 1-4 and the neutral tone (NT).Syllables were analysed as systematic gaps, which do not appear in the lexicon because they violate phonotactic constraints, and accidental gaps, which are phonotactically sound but are absent from the lexicon.Real words and malformed syllables acted as maximally and minimally acceptable controls, respectively.Linear mixed effects models indicate that tones 1-4 do not modulate acceptability judgements.NT had a significant negative effect, but this likely arises from exposure to excised neutral tone syllables out of context rather than ungrammaticality.We suggest that Mandarin non-words can be associated with any lexical tone without concern for its effect on acceptability but that neutral tone stimuli should be presented in context to preserve authenticity.
How are concepts related to fundamental human experiences organized within the human mind? Our insights are drawn from a semantic network created using the Cross-Linguistic Database of Polysemous Basic Vocabulary, which focuses on a broad range of senses extracted from dictionary entries. The database covers 60 basic vocabularies in 61 languages, providing 11,841 senses from 3736 entries, revealing cross-linguistic semantic connections through automatically generated weighted semantic maps. The network comprises 2941 nodes connected by 3573 edges. The nodes representing body parts, motions, and features closely related to human experience occupy wide fields or serve as crucial bridges across semantic domains in the network. The polysemous network of basic vocabularies across languages represents a shared cognitive network of fundamental human experiences, as these semantic connections should be conceived as generally independent of any specific language and are driven by universal characteristics of the real world as perceived by the human mind. The database holds the potential to contribute to research aimed at unraveling the nature of cognitive proximity.
Emotion recognition from visual stimuli has emerged as a crucial area of research with wide applications in the field of Human-Computer Interaction (HCI) and mental health monitoring. Understanding and predicting emotional responses to visual stimuli from images is a critical task in affective computing. Our study uses deep learning and classical machine learning techniques to classify emotions based on color images. The OASIS image dataset was used; it contains multiple themes of images, including objects, scenes, persons, and animals, with their respective arousal and valence ratings. We applied k-means clustering to identify the number of data points in the maximum cluster within those ratings. We used a Convolutional Neural Network (CNN) regressor for feature extraction of images with their ratings and separately evaluated the error metrics of both the CNN and Random Forest regressor. The results imply that the CNN regression model performs better when predicting emotional dimensions than the Random Forest regression model. This model achieves lower MAE, MSE, and RMSE across the metrics. It shows a more precise and reliable performance in capturing the complexity of emotional dimensions.
Natural Language Processing (NLP) has transformed human-machine communication in the digital age, enhancing productivity and unlocking a wealth of possibilities. The effectiveness of NLP hinges on the availability of robust digital resources, such as extensive lexical databases and real-world language corpora. These resources are crucial for various NLP applications, including machine translation, text mining, and speech recognition.NLP's advancements hold immense promise to bridge communication gaps across cultures, provide deeper linguistic insights, and boost productivity across sectors, impacting education, industry, and economic development. However, challenges such as ethical concerns, the necessity for high-quality data, and potential biases in digital language resources must be addressed.This paper presents a vision for the digital resource industry as the cornerstone of NLP, focusing on quantitative transformations that tackle NLP challenges and facilitate big data management. Embracing these transformations, along with a robust digital resource industry, can significantly enhance human-machine interactions and drive future innovations.
Accurate terminology translation is crucial for the global dissemination and comprehension of Traditional Chinese Medicine (TCM). This study addresses the pressing issue of inconsistent TCM terminology, which has led to significant confusion and misinterpretation among scholars and practitioners worldwide. The primary objective of this research is to evaluate the effectiveness of Hu Gengshen’s theoretical framework in ensuring precise and culturally appropriate translations of TCM diagnostic method terms. Hu Gengshen’s framework emphasizes three essential dimensions of translation: communicative, linguistic, and cultural. The communicative dimension focuses on aligning translations with the target audience’s linguistic norms, ensuring the translated terms are accessible and understandable. The linguistic dimension preserves the original structure and style of TCM terminology while adapting it to the target language, maintaining technical accuracy. The cultural dimension guarantees that translations respect and reflect the original cultural context, enhancing cultural sensitivity and relevance. This study employs Hu Gengshen’s framework to assess translations from authoritative TCM sources. The analysis reveals that 146 out of 152 evaluated terms meet the rigorous standards set by the framework across all three dimensions. This finding demonstrates the framework’s effectiveness in producing accurate and culturally sensitive translations. The implications of this research are significant. By validating Hu Gengshen’s framework, the study provides a practical tool for improving the clarity and precision of TCM terminology in international academic and clinical contexts. This enhancement facilitates better global engagement with TCM practices, bridging communication gaps and fostering a deeper understanding of TCM principles and methodologies. The study highlights the framework’s value in addressing translation challenges and advancing the global dissemination of TCM.
Pre-trained Language Models (PLM) have enabled a cost-effective approach to handling various downstream applications via Parameter-Efficient-Fine-Tuning (PEFT) techniques. In this context, service providers have introduced a popular fine-tuning-based product service known as Model-as-a-Service (MaaS). This service offers users access to extensive PLMs and training resources. With MaaS, users can fine-tune, deploy, and utilize their customized models seamlessly, leveraging a one-stop platform that allows them to work with their private datasets efficiently. However, this service paradigm has recently been exposed to the possibility of leaking user private data. To this end, we identify the data privacy leakage risks in MaaS-based PEFT and propose a Split-and-Privatize (SAP) framework, mitigating the privacy leakage by integrating split learning and differential privacy into MaaS PEFT. Furthermore, we propose Contributing-Token-Identification (CTI), a novel method to balance model utility degradation and privacy leakage. As a result, the proposed framework is comprehensively evaluated, demonstrating a 65% improvement in empirical privacy with only a 1% degradation in model performance on the Stanford Sentiment Treebank dataset, outperforming existing state-of-the-art baselines.
Traditionally, emotions in dreams have been assessed using subjective ratings by human raters (e.g., external raters or dreamers themselves). These methods have extensive support and utility in dream science, yet they have certain innate limitations due to the subjective nature of the rating methodologies. Attempting to circumvent several of these limitations, we aimed to develop a novel method for objectively classifying and quantifying sequential (word-for-word) emotion within a dream report. We investigated whether sentiment analysis, a branch of natural language processing, could be used to generate continuous positive and negative valence ratings across a dream. In this pilot, proof-of-concept study, we used 14 dream reports collected upon awakening following overnight polysomnography. We also collected pre- and post-sleep affective data and personality metrics. Our objectives included demonstrating that (1) valence ratings derived from sentiment analysis (Valence Aware Dictionary for sEntiment Reasoning [VADER]) could be used to visualize (plot) positive and negative emotion fluctuations within a dream, (2) how the visual properties of emotion fluctuations within a dream (peaks and troughs, area under the curve) can be used to generate novel "emotion indicators" as proxies for emotion regulation throughout a dream, and (3) these emotion indicators correlate with sleep, affective, and personality variables known to be associated with dreaming and emotion regulation. We describe 6 novel, objective dream emotion indicators: Total number of Peaks, total number of Troughs, Positive, Negative, and Overall Emotion Intensity (composites from an "area under the curve" method using the trapezoid rule applied to the peaks and troughs), and the Emotion Gradient (a polynomial trendline fitted to the emotion fluctuations in the dream chart). The latter signifies the overall direction of sequential emotion changes within a dream. Results also showed that ⅚ emotion indicators correlated significantly with at least one existing sleep, affective, or personality variable known to be associated with dreaming and emotion regulation. We propose that the novel emotion indicators potentially serve as proxies for emotion regulation processes unfolding within a dream. These preliminary findings provide a methodological foundation for future studies to test and refine the method in larger and more diverse samples.
Purpose We explored whether (1) an informational intervention improves ratings of individuals on the autism spectrum (IotAS) in a job interview by curbing salience bias and whether expert-based influence amplifies this effect (Study 1); (2) the effect of disclosure of autism on ratings depends on a candidate’s presentation as IotAS or neurotypical (Studies 1 and 2) and (3) social desirability bias affects ratings of and emotional responses to disclosers (Study 2). Design/methodology/approach In two studies, participants, randomly assigned to experimental conditions, watched a mock job interview of a candidate presenting as an IotAS or neurotypical and reported their perception of his job suitability and selection decision. Study 2 additionally measured participants’ traits associated with social desirability bias, self-reported emotions and involuntary emotions gauged via face-reading software. Findings In Study 1, the informational intervention improved ratings of the IotAS-presenting candidate; delivery by an expert made no difference. Disclosure increased ratings of both the IotAS-presenting and neurotypical-presenting candidates, especially the former, and information mattered more in the absence of disclosure. In Study 2, disclosure improved ratings of the IotAS-presenting candidate only; no evidence of social desirability bias emerged. Originality/value We explain that an informational intervention works by attenuating salience bias, focusing raters on IotAS' qualifications rather than on their unexpected behavior. We also show that disclosure is less helpful for IotAS who behave more neuronormatively and social desirability bias affects neither ratings of nor emotional responses to IotAS-presenting job candidates.
Abstract The development of a benchmark for part-of-speech (PoS) tagging of spoken dialectal European Spanish is presented, which will serve as the foundation for a future treebank. The benchmark is constructed using transcriptions of the Corpus Oral y Sonoro del Español Rural (COSER;“Audible corpus of spoken rural Spanish”) and follows the Universal Dependencies project guidelines. We describe the methodology used to create a gold standard, which serves to evaluate different state-of-the-art PoS taggers (spaCy, Stanza NLP, and UDPipe), originally trained on written data and to fine-tune and evaluate a model for spoken Spanish. It is shown that the accuracy of these taggers drops from 0.98 $$-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>-</mml:mo> </mml:math> 0.99 to 0.94 $$-$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>-</mml:mo> </mml:math> 0.95 when tested on spoken data. Of these three taggers, the spaCy’s trf (transformers) and Stanza NLP models performed the best. Finally, the spaCy trf model is fine-tuned using our gold standard, which resulted in an accuracy of 0.98 for coarse-grained tags (UPOS) and 0.97 for fine-grained tags (FEATS). Our benchmark will enable the development of more accurate PoS taggers for spoken Spanish and facilitate the construction of a treebank for European Spanish varieties.
Phrygian-KUL is a treebank of the ancient Phrygian language for Universal Dependencies (UD). Having originally only annotated the New Phrygian subcorpus, this dataset is continuously being updated to include the entire epigraphic corpus. For more information, please visit the relevant page at the UD project site or the repository on Github.
It has been suggested that humans use summary statistics such as the average of the emotion of individual faces when they rapidly judge group emotion. Previous studies have mainly used faces of actors posing basic emotions, and morphed versions of these faces, against a plain background. In the present study, photographs taken in real-world settings were used to investigate the influence of mean facial emotion, maximal facial emotion, and background context on judgments of group emotion, assessed using dimensional ratings of valence, arousal, and dominance. Background context explained a significant amount of unique variance in group ratings for each dimension. Mean emotion explained additional unique variance for valence ratings, whereas maximal emotion explained additional unique variance for arousal, with dominance showing more mixed results. Removing background context and disrupting the contextual and spatial relationship between faces by randomly replacing faces with ones from other images within the stimulus set increased reliance on mean emotion. However, under all conditions, the maximally arousing face continued to exert an influence on ratings of group arousal, in line with theoretical accounts arguing for a unique bottom-up effect of emotional arousal on attentional competition and postattentive perceptual processing. Together these findings suggest that individuals' reliance on average emotion when judging crowd scenes differs as a function of the dimension of affect. In addition, the presence of background context both directly impacts judgments of crowd emotion and modulates the relative influence of maximal versus mean emotion on these judgments. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
Emotional experiences deeply impact our bodily states, such as when we feel 'anger', our fists close and our face burns. Recent studies have shown that emotions can be mapped onto specific body areas, suggesting a possible role of the primary somatosensory system (S1) in emotion processing. To date, however, the causal role of S1 in emotion generation remains unclear. To address this question, we applied transcranial alternating current stimulation (tACS) on the S1 at different frequencies (beta, theta, and sham) while participants saw emotional stimuli with different degrees of pleasantness and levels of arousal. Results showed that modulation of S1 influenced subjective emotional ratings as a function of the frequency applied. While theta and beta-tACS made participants rate the emotional images as more pleasant (higher valence), only theta-tACS lowered the subjective arousal ratings (more calming). Skin conductance responses recorded throughout the experiment confirmed a different arousal for pleasant versus unpleasant stimuli. Our study revealed that S1 has a causal role in the feeling of emotions, adding new insight into the embodied nature of emotions. Importantly, we provided causal evidence that beta and theta frequencies contribute differently to the modulation of two dimensions of emotions-arousal and valence-corroborating the view of a dissociation between these two dimensions of emotions.