Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
Abstract Dependency distance (DD) and hierarchical distance (HD) measure syntactic complexity linearly and hierarchically, reflecting comprehension and production preferences, respectively. Using syntactic dependency treebanks of English e-commerce live-streaming by American (CAL) and Chinese (CCL) hosts, this study compares the two hosts’ syntactic complexity via mean dependency distance (MDD) and mean hierarchical distance (MHD). The results show significant MHD differences between CAL and CCL but no MDD differences. Both treebanks follow Zipf’s law in DD/HD distributions, with short sentences dominating. While nine dependency types overlap in the top 10 frequent relations across three sentence lengths, some dependency types, such as discourse and aux, are more frequent in CAL. Notably, CCL exhibits significantly higher MHDs, a finding that is mainly attributed to structures like complex sentences, coordinate clauses, that -clauses, and quantifier phrases. The findings further suggest a universal tendency to minimize syntactic complexity in English live-streaming, with native language transfer primarily influencing hierarchical complexity (MHD).
This article introduces the concept of Normative Phonetic Inclusiveness (NPI), defined as the degree to which a speech community recognizes multiple phonetic realizations of the same phonological category as equally legitimate within a shared linguistic norm. Using the Dutch rhotic (/r/) system as an exploratory case, the paper argues that the sociolinguistic significance of phonetic variation extends beyond its role as a marker of social identity to include the social organization of linguistic legitimacy. Contemporary Dutch accommodates a wide range of rhotic realizations without systematically privileging a single variant, providing an opportunity to examine how linguistic communities regulate diversity within a stable normative framework. Rather than proposing a causal relationship between pronunciation and broader cultural values, the article suggests that linguistic norms and other social norms emerge within the same historical and institutional ecology. Drawing on sociolinguistics and classical sociological theories of norms, the study conceptualizes NPI as a property of speech communities rather than individual speakers and distinguishes it from established concepts such as phonetic variation, linguistic prestige, and language ideology. The Dutch case serves as a theoretically motivated illustration for developing a broader framework of linguistic legitimacy, with the aim of stimulating future comparative research on how different speech communities organize the acceptance of phonetic diversity.
Language-model representations provide structured, high-dimensional annotations of naturalistic language stimuli and can serve as informative neural predictors during comprehension. We analyzed locked derived data from Brain Treebank, MEG-MASC, and Podcast ECoG with eight frozen language models, blocked encoding models, and matched temporal, nuisance, and representation-capacity controls. Positive held-out prediction and gains over low-level baselines were widespread in source-level summaries. Across Brain Treebank and Podcast ECoG, 67 of 432 evaluable rows met a controlled predictive-only criterion, and model-side feature ablations changed prediction scores in most evaluable source rows. Brain-derived, timing-linked, acoustic, and implanted-signal controls confirmed component-level sensitivity of the analysis pipeline. These findings show that language-model-derived quantities can annotate neural activity during natural speech and text comprehension. Participant-level matched-control advantages were localized rather than uniform, response-profile and feature-specificity contrasts bounded representational or computational interpretations, and complete co-indexed integrated interpretation will require future jointly indexed coverage. Together, the analyses identify language-model features as useful neural predictors and separate predictive usefulness from claims about shared neural organization or language-processing computations.
Background: Dense cross-layer connectivity can shorten gradient paths and promote feature reuse, potentially improving optimization under fixed training budgets. Objective: We test whether concatenation-based dense historical connectivity improves decoder-only autoregressive language modeling under controlled comparison protocols. Methods: We compare a standard Transformer decoder and a dense decoder on Penn Treebank and WikiText-2 under two fairness regimes: (i) a same training recipe setting with a fixed baseline and a bounded dense architectural search, and (ii) a same parameter budget setting where the dense model is resized to not exceed the baseline parameter count. Results: Dense connectivity does not consistently reduce test perplexity; on WikiText-2, the baseline remains better in both regimes, while gains on Penn Treebank are small and regime-dependent. Ablations within the dense family show that depth and feed-forward capacity are the most reliable drivers of perplexity improvements. Conclusions: Probes and attention diagnostics do not reveal a clear advantage for dense connectivity in our limited probe set, while Zipf–RQA analysis of long-form generations reveals systematic structural differences between baseline and dense outputs. Specifically, Zipf–RQA is used here as a descriptive structural probe rather than a performance metric.
This research paper investigates the transformative role of Artificial Intelligence (AI) in enhancing the academic writing proficiency of future English language teachers. As academic writing demands rigorous adherence to linguistic norms, logical structuring, and critical engagement with scientific discourse, traditional pedagogical methods often struggle to provide immediate, personalized feedback. This study explores the utility of the Grammarly program as an autonomous learning assistant. The findings demonstrate that integrating AI tools into the curriculum not only rectifies surface-level grammatical errors but also fosters meta-cognitive awareness, enabling students to identify their own recurring mistakes and improve their writing fluency, stylistic accuracy, and adherence to formal academic standards. Furthermore, this study proposes a comprehensive framework for integrating such tools to bridge the gap between novice and professional academic writers.
This article examines the protection of the Azerbaijani language in the context of globalization and digitalization as a priority area of public policy from a scientific and analytical perspective. The study analyzes the interaction of languages in the modern information environment, the impact of social media and digital technologies on national languages, and the opportunities and risks posed to the Azerbaijani language. The role of language in protecting national identity, statehood, and national-spiritual values through state programs, decrees, and orders, as well as legal and institutional mechanisms, is substantiated. The article highlights measures taken to strengthen the position of the Azerbaijani language in the digital environment, improve the terminology system, protect linguistic norms in the media and educational environment, and address existing risks. The study demonstrates that protecting the Azerbaijani language is not only a cultural issue but also has strategic significance in terms of national security, national identity, and statehood.
This article focuses on improving the language skills of future professionals in Ukrainian language classes tailored to their specific fields. It has been established that Ukrainian language instruction tailored to specific professional fields is a key tool for enhancing language skills, facilitating the development of professional competence, adherence to linguistic norms, mastery of a business style, and effective communication. It is emphasised that the teaching profession places very high demands on specialists: a teacher must be a unique, vibrant personality, a repository of diverse and profound knowledge, and possess a sufficiently high level of cultural refinement. Therefore, future specialists must have a perfect command of Ukrainian linguistic etiquette, as the teacher’s personal example will help students develop the skills of linguistic communication. It has been established that the main areas for improving communication skills include: adherence to linguistic norms; mastery of specialist terminology; development of professional communication; observance of communication etiquette; and the development of logical and clear expression. The following methods are used in practical sessions: business games and role-play scenarios; text analysis; discussions and debates; drafting and editing documents; and the use of multimedia resources. The Ukrainian language for professional purposes is an important component of professional training, ensuring the high level of intellectual and linguistic proficiency required for a successful career. To successfully deliver the curriculum, a primary school teacher must not only have a thorough understanding of language teaching methodology, but also serve as a model of standard speech. This entails impeccable literary pronunciation, the correct use of vocabulary and grammatical structures, as well as skilful control of intonation during spoken communication and reading. In other words, teachers must pay equal attention to both the content and the form of their speech. Unlike professionals in other fields, teachers use language as a public tool that serves as a benchmark for their pupils.
Prediction systems grounded in textual data have become indispensable across high-stakes domains including clinical decision support, financial signal detection, and digital misinformation analysis. Classical statistical approaches and shallow machine learning methods have demonstrated satisfactory performance on narrow, well-curated datasets, but they struggle to generalise once input distributions shift or domain vocabulary diverges from training corpora. Deep learning, and more specifically the pre-trained transformer paradigm, has substantially narrowed this gap; nevertheless, single-architecture solutions routinely leave accuracy on the table when applied to tasks that demand both rich contextual encoding and explicit sequential reasoning. This paper presents a cohesive, end-to-end AI- powered prediction framework that fuses BERT- derived contextual representations with a two-layer bidirectional LSTM (BiLSTM) classification head augmented by an additive attention mechanism. The system is designed as a modular pipeline: text acquisition and normalisation, augmentation-based imbalance handling, deep encoding, sequential modelling, and post-hoc probability calibration are treated as independent, replaceable stages. Experimental evaluation across three publicly available benchmark datasets — the LIAR fake news corpus, Stanford Sentiment Treebank v2, and a health-claim verification collection — confirms that the hybrid BERT-BiLSTM-Attention architecture outperforms five competitive baselines on macro-averaged F1 and area under the ROC curve. Ablation experiments quantify the individual contributions of the attention layer, recurrent head, augmentation strategy, and temperature scaling. A discussion of deployment trade-offs addresses inference latency, continual adaptation, and algorithmic fairness..
The purpose of the article is to identify the features of communicative practices implemented in the educational content of Ukrainian language TikTok blogs and to determine their role in the development of users’ language skills in the online communicative environment. Research methodology involved the use of the following methods: content analysis – to identify communicative practices for promoting the Ukrainian language on TikTok; analysis and synthesis, observation, and generalisation – to process educational linguistic content and identify communicative strategies for audience engagement; sociocultural analysis – to outline the role of social networks in shaping language identity and collective awareness of linguistic norms. Scientific novelty of the article lies in the comprehensive analysis of communicative practices of TikTok blogs promoting the Ukrainian language as a factor in the development of users’ language skills, in presenting interactive strategies used by bloggers to engage audiences in mastering language norms, and in forming language identity. Conclusions. Communicative practices of TikTok bloggers develop language skills through interactive communication, word games, and role-playing dialogues, involving the audience in joint analysis of the features of the Ukrainian language. Users become co-creators of content: they comment, suggest alternative forms, create humorous mnemonic constructions, and analyse the presented examples, thereby becoming aware of the cultural and historical context of linguistic norms. Such communicative technologies take the form of collective language activity, shaping language identity and strengthening the sense of belonging to the linguistic community. The format of online platforms, particularly TikTok – short videos, recommendation algorithms, interactivity, and virality – contributes to the promotion of language culture. At the same time, the specifics of clip-based thinking and the tendency toward conciseness may lead to fragmented presentation of material and simplified representation of complex historical-linguistic processes. Digital communicative practices act as a factor in the linguistic mobilisation of Ukrainian society, while simultaneously creating potential challenges in the context of preserving and disseminating language norms.
Abstract Emotional coherence between subjective experience and autonomic physiological activation is a key topic in emotion research, but how the coherence differs across cultures remains poorly understood. The present study explored differences in self-reported emotional responses, skin conductance responses, and their coupling in Eastern and Western participants (N = 43). Two pieces of classical music were used for emotion induction. The Aligned Rank Transform ANOVA analysis revealed significant Culture × Group interactions for both changes in self-reported valence ( p = .017) and arousal ratings ( p = .033). Higher emotional coherence was observed in Western than in Eastern participants, particularly under the negative music condition, though not statistically significant. These findings provide preliminary evidence that culture may play a role in differentially shaping emotion regulation and highlight the importance of including culture as a variable in emotion research.
Natural animal sounds can be perceived as "harsh" or "buzzy"-sound descriptions known as auditory roughness. In this study, the association between the human emotional appraisal of animal vocalizations and the perceived roughness was examined across three sound categories-mammals, birds, and insects. Ninety 1-s vocalizations (30 per category) were rated online by two independent groups: one judged perceived roughness, the other judged valence and arousal. Perceived roughness showed a strong negative correlation with valence (rougher sounds were judged more negatively). No clear link between roughness and arousal emerged (no correlation for birds and mammals, and a small one for insects) probably because of the homogeneity of the arousal ratings. Acoustic analyses showed that a variability measure derived from the modulation power spectrum tracked perceived roughness and, inversely, valence within and across categories. Together, these results indicate that humans systematically interpret rough sounds, irrespective of their source species, as cues of negative valence. We propose that roughness is an ecologically meaningful auditory code that drives emotional responses to animal sounds in humans.
This study examined how pre-listening information influences music appreciation among 107 Japanese junior and senior high school students. Two songs were used: The Italian Sogno, where musical tone aligns with lyrics, and the German Im wunderschönen Monat Mai, where they do not align. Participants were assigned to three groups differing in the amount of prior information: none (“No Information Group”), brief lyric explanations (“Lyrics Explanation Group”), and detailed explanations including lyrics, background, and acoustic features (“Lyrics and Background Explanation Group”). When lyrics and tone were incongruent, the No Information Group’s emotional valence ratings aligned more with the tone than did those with prior information; this effect was absent in the congruent condition. Open-ended responses showed the No Information Group focused on surface features like the languages of lyrics rather than thematic content. These findings highlight the educational value of emphasizing lyric understanding in Japanese music education.
Abstract: The integration of artificial intelligence (AI) into English as a Foreign Language (EFL) education has brought about transformative changes in how learners develop intercultural communicative competence (ICC). This systematic literature review examines how AI-mediated language production and adaptive feedback mechanisms reshape ICC among EFL learners. Following PRISMA guidelines, this study analysed 35 peer-reviewed articles published between 2020 and 2025. The review focuses on ELT-relevant dimensions, including automated writing evaluation, generative AI in language learning, and AI-mediated cross-cultural exchange. Findings indicate that AI facilitates ICC by providing real-time adaptive feedback that helps learners negotiate cultural nuances and linguistic norms. The study concludes that AI serves as a "cultural mediator," offering a triadic interaction model that enhances learners' knowledge, skills, and attitudes in intercultural settings.
This study investigates whether the relationship between word-level stress detection and musical aptitude differs across first-language (L1) backgrounds.While prior work has shown that musical aptitude predicts prosodic sensitivity, it remains unclear whether this relationship is modulated by linguistic experience.Sixty intermediate-to-advanced learners of Spanish (20 German, 20 French, 20 Korean) completed an odd-one-out stress discrimination task and the Mini-PROMS musical aptitude test assessing Melody, Accent, Tempo, and Tuning.Spanish word-familiarity ratings served as a proficiency control.Results showed that German learners outperformed Korean and French participants, consistent with cross-linguistic differences in the use of lexical stress.Among the musical subtests, only Melody significantly related to stress discrimination across all L1s, suggesting a shared sensitivity to pitch variation.The Accent score interacted with L1, showing a positive trend for German and French learners but not for Korean participants.No effects emerged for Tuning or Tempo.These findings indicate that pitch-related perceptual skills, rather than rhythmic or timbral ones, relate to stress sensitivity in L2 Spanish, highlighting the role of specific musical dimensions in prosodic learning.
The study explores the attitudes and opinions of Pakistani English teachers on Standard British English (SBE) and Standard American English (SAE). Although most studies have been done on learner attitudes, this paper refracts the same to the teacher, whose role is very important in influencing linguistic norms in EFL. A survey involving 60 English teachers in the government and privately owned institutions in Pakistan was conducted using a mixed-method approach, whereby a questionnaire, which included closed-ended and open-ended questions, was used to gather the data. The results indicate that there is an acute effect of academic preparation of teachers, exposure to media, and the practices of the institution on the preference of variety among the teachers. The paper also examines the relationships between the linguistic backgrounds and pedagogical decision-making amongst teachers. The findings can be added to the current discussion of World Englishes and can be applied to the teaching training and language policy in Pakistan.
This study investigates the integration of dialectal features into official Czech toponyms, with a particular focus on street names and non settlement names. It explores how dialectal elements persist in official naming practices despite standardization efforts, especially in regions with strong dialect traditions. The authors analyze the linguistic and administrative processes behind toponym standardization and highlight the discrepancies between different mapping platforms – namely the state-run Geoprohlížeč and the commercial mapy.com. While street names are regulated and recorded in the national database (RÚIAN), non-settlement names lack centralized oversight, resulting in greater variability. The paper identifies specific phonological and morphological dialectal features that appear in official names, often due to the direct adoption of local spoken forms. The authors argue for a balanced approach to standardization that respects both linguistic norms and regional identity, emphasizing the cultural and communicative significance of toponyms in public space.
Abstract Research on how non-natives process and learn binomials ( black and white ) is limited. The present study addresses this gap using online (eye-tracking) and offline (familiarity rating) tasks. Sixty non-native speakers of English (L1 = Arabic) read six stories seeded with 21 novel binomials in three conditions: one exposure, six exposures, and no exposure (i.e., only in post-test) in a counter-balanced design. Each item was also presented in the reversed order ( white and black ). The non-natives read the stories as their eye movements were monitored and answered comprehension questions. In addition to the novel binomials, 12 existing binomials (congruent with Arabic) were included in the passages as a baseline for comparison. After completing the reading task, the participants completed an offline rating task as a measure of declarative knowledge of the binomial configuration (i.e., word order). All items were rated twice, once in the forward direction and once in the reversed direction. Online results showed that non-natives were not sensitive to the configuration of existing binomials, and there was limited evidence of any sensitivity to novel binomials. Offline, non-natives showed sensitivity to the configuration restrictions of existing binomials but not novel ones.
In Spanish linguistics today, it is widely recognized that Spanish corresponds to the image of a pluricentric language. Different normative centers coexist, which are perceived as such by the speakers. However, the degree of recognition, status, and prestige of these linguistic norms varies greatly. This article examines the extent to which the pluricentrism of Spanish is represented in textbooks for Spanish as a foreign language. As a case study, three textbooks – Puente Nuevo, ¡Adelante!, and A_tope.com – used in high schools in Basel, Switzerland, are analyzed both quantitatively and qualitatively in terms of their pluricentric character. The study examines the textbooks as a whole (thematic priorities, topics of units, maps) and selected linguistic phenomena, namely: the forms of address (morphosyntax), seseo (pronunciation), and the use of regional vocabulary. At various levels, it can be demonstrated that the textbooks are based on a Eurocentric view of language, which gives Castilian Spanish a superior role to that of other language norms, without this being explicitly stated. The article concludes with some practical recommendations for a more pluricentric approach to teaching Spanish.
We revisit punctuation-aware tree binarization for constituency parsing and ask whether dependency-induced headedness improves binary parser supervision. Although learned heads substantially outperform rule-based heads in intrinsic head prediction, they do not yield consistent parsing gains after debinarization. In particular, punctuation-conditioned evaluation shows that learned headedness underperforms rule-based binarization in macro-average punctuation-sensitive $F_1$, despite a small overall gain on CTB. Similar instability appears under cross-treebank transfer. These results suggest that \ycc{linguistically grounded} headedness is not necessarily parser-optimal when used as a binarization control signal. The paper presents a negative result: better head prediction does not imply better punctuation-sensitive constituency parsing.
Affective word values have been widely studied across languages, often focusing on isolated words due to the difficulty of assessing emotionality in texts. This study examines whether written emotional content can be reliably captured using a specific software tool (Watson Natural Language Understanding). Thirty-three Spanish undergraduates wrote 150-word autobiographical texts in their L2 (English) before and after a training with emotional vocabulary. Normative valence ratings of content words obtained in the pre- and post-training phases were compared with sentiment scores generated by Watson NLU. Strong positive correlations were found between sentiment and normative valence scores in both phases, with stronger relations at post-training. Regression analyses confirmed that sentiment scores significantly predicted normative valence. Importantly, while normative valence did not differ between phases, sentiment scores increased after training. These results suggest that Watson NLU is a valid and sensitive tool for assessing emotionality in written language and its modulation through training.
• Combined EEG, voice morphing, ERP, mTRF and MVPA to unravel neural mechanisms of ambiguous attitudinal vocal expression processing. • Pinpointed LSN (700–1600 ms) as the core neural signature distinguishing ambiguous from typical attitudinal voices. • Discovered early–late functional coupling, challenging serial models by linking acoustic encoding to late socio-cognitive inference. • Dissociated acoustic-driven (N1/P2) and valence-specific (LSN) effects via covariate-controlled LMM and mTRF analyses. • Extended multi-stage prosody models to attitudinal processing, integrating ambiguity in real-world social communication. Vocal attitudes (e.g., confidence, desire) convey rich acoustic cues that transmit speaker's intentions and beliefs, playing a pivotal role in natural speech communication. Neurocognitive research has largely centered on inferring attitudes from voices with unambiguous, clearly defined meanings (“typical voices”), while the neural mechanisms underlying ambiguous voices remain underexplored—particularly compared to vocal emotions, leaving a critical gap in understanding paralinguistic socio-cognitive processing. Here, we employed voice morphing to blended two typical attitudinal voices of opposing valences, recording participants’ valence ratings and electroencephalographic (EEG) responses. Data analysis combined conventional ERP analysis using linear mixed-effects modeling on single-trial data with multivariate approaches (multivariate temporal response function [mTRF], multivariate pattern analysis [MVPA]). Behaviorally, ambiguous voices elicited longer reaction times and intermediate valence ratings. Neurally, ambiguous voices showed a P2 (274–324 ms) resembling positive voices, an N400-like negativity (400–450 ms) resembling negative voices, and a robust a Late Sustained Negativity (LSN; 700–1600 ms) distinct from typical voices. Controlling for acoustic parameters eliminated early effects (N1/P2/N4), confirming they reflect acoustic processing, while the LSN persisted—indexing neural responses to attitudinal ambiguity. mTRF validated stronger late-stage neural tracking of ambiguous voices after accounting for acoustics; MVPA revealed cross-temporal early-late functional coupling between acoustic encoding and pragmatic inference. Together, these findings demonstrate the brain treats ambiguous attitudinal prosody as a distinct category, engaging a specialized cascade: enhanced early acoustic discrimination, graded valence evaluation, refined semantic processing, and effortful pragmatic inference. This work extends multi-stage models from emotional to attitudinal prosody, challenging strictly serial accounts by highlighting interactive neural dynamics in ambiguity resolution.
This article analyzes the systemic crisis of Arabic culture from the 13th to 18th centuries and the pivotal role of Arab Christians in initiating the Arab Renaissance. The author explores the causes of stagnation in Muslim society, primarily the institutional dominance of taqlīd (imitation) and the widening gap between sacralized linguistic norms (fuṣḥa) and living speech. Central to the study is the scholarly contribution of the Maronite scholar Ibn Farhat, whose work bridged Western rationalism and Eastern tradition. Special focus is placed on his treatise «Baḥṯ аl-maṭālib wа ḥаṯṯ аl-ṭālib», which simplified Arabic pedagogy and integrated the language into the daily and liturgical practices of Christian communities. The paper emphasizes that the transition from Karshuni script to classical Arabic, alongside Ibn Farhat’s reforms, provided the ideological foundation for overcoming cultural isolation. It concludes that Lebanon’s Christian intellectuals, educated through European models like the Pontifical Maronite College, were the primary catalysts for modernizing Arabic philology and precursors to the 19th-century Enlightenment.
Child-directed fingerspelling is an approach used by Deaf parents for communication, language, and literacy development. This study reports on findings from a qualitative intrinsic case study aimed at understanding how Deaf parents use fingerspelling with their young children. The research questions were: (1) What are the cultural beliefs of Deaf parents regarding fingerspelling with young children? (2) What are their patterns of use of child-directed fingerspelling in natural settings? Twenty-one Deaf families with 27 deaf children ages 5 years and under were interviewed via recorded Zoom meetings conducted in American Sign Language. Data were analyzed using grounded theory to develop a new theoretical contribution with the core category: Deaf families socialize their children into Deaf visual-linguistic norms through fingerspelling. This new theoretical insight aligns with Holcomb's Deaf epistemological framework (2010) and Ochs and Schieffelin's (2008, 2011) language socialization theory. Limitations and recommendations for future research are also included.
AI-mediated communication refers to communicative processes in which artificial intelligence systems actively generate, interpret, modify, or facilitate language. With the rapid advancement of language technologies such as large language models, conversational agents, speech recognition systems and machine translation tools, AI has evolved from a supportive tool into an active mediator of human communication. This paper conceptualizes AI-mediated communication as a form of language technology that reshapes traditional models of interaction, meaning-making and authorship. The study examines the technological foundations of AI-driven language processing and their broader socio-cultural, pedagogical and ethical implications. It explores how AI mediation influences linguistic norms, accessibility and power relations, while also raising critical concerns related to bias, surveillance and linguistic homogenization. By situating AI-mediated communication within contemporary digital culture, the paper argues that understanding AI as a language technology is essential for critically engaging with evolving communicative practices and for developing responsible, inclusive and ethically grounded AI-driven communication systems.
This research explores the historical emergence of linguistic terminology in three languages—English, Uzbek, and Karakalpak—with special attention to the role of Latin, Greek, and Arabic heritage. It traces how borrowed concepts were nativized and localized in each linguistic setting. By juxtaposing five evolutionary stages in English with analogous processes in Uzbek and Karakalpak, the paper illustrates the interplay between international scholarly traditions and indigenous linguistic norms. The conclusions highlight both universal tendencies and language-specific particularities in the growth of terminological systems.
This study investigates the linguistic and communicative functions of abbreviations in English and Karakalpak advertising discourse, focusing on how these compressed forms contribute to message efficiency, stylistic expression, and cultural positioning. Although abbreviations are widely used across global advertising, their structural patterns and pragmatic roles vary according to linguistic norms and audience expectations. Therefore, the research employs a mixed qualitative methodology integrating structural analysis, discourse interpretation, and comparative linguistics. The results demonstrate that English advertising makes extensive and creative use of acronyms, initialisms, blends, and hybrid forms to construct modern, technologically oriented, and globally recognizable brand identities. In contrast, Karakalpak advertising relies more on functional initialisms and borrowed English abbreviations, reflecting both local communicative preferences and growing global influence. The discussion interprets these findings within broader socio-cultural and economic contexts, revealing that abbreviation usage serves as a marker of globalization, cultural continuity, and linguistic innovation. Ultimately, the study contributes to a deeper understanding of how abbreviated forms shape contemporary advertising communication in multilingual environments.
Pre-trained language models (PLMs) achieve high accuracy on standard benchmarks for sentiment analysis. However, this performance can hide systematic weaknesses in determining the sentiment of negated sentences, for example when the phrase “not good” is still classified as positive. In this study, we use sentiment classification of English movie reviews in the Stanford Sentiment Treebank 2 (SST-2) as a case study to specifically examine and improve how BERT handles negated sentences. We perform a brief additional fine-tuning of the existing BERT model on a small, automatically constructed set of lexicon-based counterfactual examples that target simple lexical negation. Experimental results on carefully paired original-negated sentences show that this procedure substantially reduces prediction errors on negated inputs while leaving overall performance on SST-2 almost unchanged.
This study aimed to enhance the practical utility, in real operating contexts, of warning sounds designed to prevent declines in train drivers’ arousal. We analyzed subjective ratings of audibility and arousal for warning sounds presented at 75 dB under varying running noise levels (from 65 dB to 85 dB), considering age as the grouping factor. Experiment 1 (general participants, five noise levels × five warning types (four arousal-designed, one monotone)) showed that increasing noise decreased audibility and, in turn, arousal ratings; moreover, the ≥40s group reported higher arousal than the 20s–30s group. Under high-noise conditions, lower-frequency arousal-designed warning sounds were rated lower than the monotone, such that ratings could fall to levels at which neither audibility nor perceived arousal was attained. Experiment 2 (train drivers, two noise levels × three warning types showed the same qualitative tendency, providing ecologically relevant support for the Experiment 1 finding that low-frequency arousal-designed warnings perform poorly under high noise; no clear age-group difference was observed. Experiment 3 (in-service vehicles) lends support to the findings of Experiments 1 and 2. Overall, under high running noise, warning sound effectiveness depends on frequency, suggesting that designing accordingly can improve practicality even for arousal-designed sounds.
Replication package for "CEI: A Benchmark for Evaluating Pragmatic Reasoning in Language Models" (DMLR 2026). The Contextual Emotional Inference (CEI) Benchmark is a dataset of 300 expert-authored scenarios for evaluating how well language models interpret pragmatically complex utterances in social contexts. Each scenario presents a communicative exchange involving indirect speech (sarcasm, mixed signals, strategic politeness, passive aggression, or deflection) where the speaker's literal words diverge from their actual emotional state. Three trained annotators independently labeled every scenario using Plutchik's 8 basic emotions and Valence-Arousal-Dominance ratings. This archive contains: • data/human-gold/ — 5 merged annotation CSVs (300 scenarios, 3 annotators each) • scripts/ — Pipeline, analysis, and HuggingFace upload scripts • config/ — Model definitions and pricing configuration • papers/dmlr2026/ — Paper source (LaTeX), bibliography, and figures • reports/dmlr2026/ — Baseline results (JSON) and LaTeX tables • LICENSE (MIT for code) and README.md The dataset is released under CC-BY-4.0. Code is released under MIT. GitHub: https://github.com/jon-chun/cei-tom-dataset-base HuggingFace: https://huggingface.co/datasets/jonc/cei-benchmark
The correct use of Standard Albanian in public administration is vital for effective governance, accurate communication, and the maintenance of public trust. This study explores the extent to which standard language is used in Albanian state institutions through questionnaires and interviews with employees of the Ministries of Education, Defense, and Justice. The results indicate that official documents often contain linguistic errors and inconsistencies, reflecting shortcomings in language precision. These issues are largely caused by the absence of standardized document templates, insufficient attention to linguistic norms, and limited opportunities for staff training. Nearly one-third of respondents reported that errors frequently occur in emails and reports addressed to citizens.To address these challenges, the study emphasizes the importance of digital technologies, standardized communication models, and institutional reforms. The use of digital platforms with grammar-checking tools and shared templates can improve consistency and accuracy in official documents. In addition, promoting lifelong learning through continuous professional development and linguistic training can strengthen institutional efficiency, accountability, and public trust in public administration. Received: 06 October 2025 / Accepted: 12 December 2025 / Published: January 2026
Background: Aging is characterized by a decrease in olfactory, attentional, memory, language, and visuospatial/executive abilities. In this context, our study aimed to evaluate the potential effects of Rosmarinus officinalis L. (rosemary) and Carum carvi L. (caraway) essential oils (EOs) on aging. First, we assessed, in 402 participants, the age-related changes in olfactory functions (odor threshold, discrimination, and identification), gustatory perceptions (sweet, sour, salty, and bitter taste), cognitive functions (focusing on attention, memory, language, and visuospatial/executive functions), and their possible correlations with aging. To achieve this, olfactory function, gustatory perception, and cognitive abilities were evaluated in healthy participants across different age groups. Then, to evaluate the age-related decrease in trigeminal function (59 participants), we used rosemary and caraway EOs that contain carvone, limonene, and 1,8-cineole, all of which are considered typical trigeminal stimuli. Methods: Olfactory function was assessed with the Sniffin’ Sticks test, gustatory function by the Taste Strips test, and rosemary and caraway EOs by the ratings of odor pleasantness, intensity, and familiarity using a labeled hedonic Likert-type scale. Results: Olfactory function could be a potential early indicator of attentional, memory, language, and visuospatial/executive dysfunctions. Our data indicated that rosemary and caraway EOs were perceived without any significant decrease in odor pleasantness, intensity, and familiarity ratings in relation to aging. Conclusion: Our results suggest the potential bioactive effects of rosemary and caraway natural EOs as a new strategy to promote healthy aging.
The Icelandic Morphosyntactic Ontology (IMO) defines a formal, AI-compatible representation of Icelandic morphosyntax, including closed morphological feature inventories, a typed dependency-style relation system, and a constraint-based validation layer. Version v0.1 establishes an architectural specification rather than a complete lexical or descriptive grammar. It introduces a formal morphosyntactic model designed for computational compatibility (e.g., NLP validation, dependency parsing alignment), while explicitly separating a derived human learnability projection layer. The document includes:(1) Morphological entity system,(2) Syntactic relation graph layer,(3) Constraint architecture (agreement, governance, default case assignment),(4) Dependency matrices,(5) A structured human learnability projection blueprint. IMO does not replace existing Icelandic lexical databases or parsing systems (e.g., BÍN, Greynir), but operates at a formal abstraction layer intended for structural modeling and constraint-based validation.
In recent years, the number of people viewing pet videos and images online has risen. Although numerous studies have shown that owning pets positively impacts human mental health, the potential mental health benefits of prolonged exposure to pet media content remain debated. This study conducted three experiments to investigate how viewing pet videos affects human emotional face processing and to clarify the associated emotional regulatory mechanisms. Experiment 1 examined how viewing pet videos influences attentional bias toward emotional faces. Experiment 2 assessed the impact of watching pet videos on the valence perception of emotional faces. Experiment 3 analyzed how exposure to pet videos affects the valence perception of emotional text. The results showed that watching pet videos increased attentional bias toward subsequent positive emotional faces and decreased bias toward negative ones. This effect resulted from higher perceived valence ratings for both positive and neutral emotional faces. Importantly, this effect was only observed in facial stimuli with social attributes. These findings indicate that watching pet videos modulates emotional processing, and prolonged exposure to pet media content may affect mental health through this mechanism.
This paper examines the growing ideological dissonance between the contemporary political left and the working-class constituencies it historically claimed to represent. Drawing on sociological and political science literature alongside comparative evidence from Brazil, the United States, France, and the United Kingdom, we argue that the left’s progressive drift toward university-incubated identity politics—including debates over linguistic norms, gender categories, and minority group identity—has produced a profound semantic and cultural rupture with lower-income voters whose priorities remain rooted in economic security, family cohesion, and social conservatism. Simultaneously, right-wing and populist movements have strategically occupied the abandoned terrain of class-based discourse, reframing their appeal in the language of workers, families, and everyday material hardship. We analyze the semantic transformation of the terms left and right, the phenomenon of cancel culture as a mechanism of epistemic coercion, the paradox of conservative sociality among the poor, and the structural conditions that produce what we term displacement populism—the rightward migration of voters who were once natural constituents of progressive politics. We conclude with reflections on the prospects for political realignment and the conditions under which left-wing movements might recover their original social mandate.
We introduce BeDiscovER (Benchmark of Discourse Understanding in the Era of Reasoning Language Models), an up-to-date, comprehensive suite for evaluating the discourse-level knowledge of modern LLMs.BeDiscovER compiles 5 publicly available discourse tasks across discourse lexicon, (multi-)sentential, and documental levels, with in total 52 individual datasets.It covers both extensively studied tasks such as discourse parsing and temporal relation extraction, as well as some novel challenges such as discourse particle disambiguation (e.g., "just"), and also aggregates a sharedtask on Discourse Relation Parsing and Treebanking for multilingual and multi-framework discourse relation classification.We evaluate open-source LLMs: Qwen3 series, DeepSeek-R1, and frontier reasoning model GPT-5-mini on BeDiscovER, and find that state-of-the-art models exhibits strong performance in arithmetic aspect of temporal reasoning, but they struggle with long-dependency reasoning and some subtle semantic and discourse phenomena, such as rhetorical relation classification.
This repository contains anonymized behavioral data, analysis scripts, and experimental code for a virtual reality (VR) study investigating affective modulation of route-based spatial navigation. The paradigm combines immersive navigation in a controlled urban VR environment with an instructed threat-of-shock (ToS) manipulation and systematically varies locomotion modes (continuous vs. stepwise movement). The dataset includes navigation performance measures (accuracy, decision times), questionnaire data (e.g., trait anxiety, presence, affective ratings), and preprocessing/analysis pipelines implemented in R. The VR experiment was developed for a head-mounted display (Meta Quest 3), and the provided scripts allow reconstruction of the experimental logic. Due to copyright restrictions, the full digital object library (e.g., 3D assets, textures) is not included.
Abstract Introduction Sleep supports emotion regulation by preferentially consolidating emotional memories while attenuating reactivity. We have shown that dream recall plays an active role by increasing negative over neutral memories and reducing reactivity. In women, fluctuating reproductive hormones across the menstrual cycle influence sleep features implicated in emotional memory, yet whether menstrual phases influence how dreams shape emotional processing remains unknown. This study investigates how dreams shape sleep-dependent emotional processing across the menstrual cycle in naturally cycling women. Methods 128 women (Mage = 32.85 ±11.93 years) completed up to four visits across verified menstrual phases (menses, late-follicular, mid-luteal, late-luteal). At each visit, participants performed the Emotional Picture Task with negative and neutral IAPS images in the evening (Test 1) and the next morning (Test 2). Participants rated old/new, arousal, and valence of images shown at each test. Dream reports were collected upon waking prior to Test 2. Linear mixed-effects models tested main and interaction effects of menstrual phase and dream recall. Results The menstrual cycle altered how dreaming shaped overnight emotional memory. Dream recall typically benefited the emotional trade-off effect —favoring consolidation of negative relative to neutral images (Δd′; t(410)=1.95, p=0.05)—but this pattern reversed during the late-luteal phase (dream × menstrual cycle: t(381)=-2.29, p=0.02). Dreaming showed independent effects on emotional reactivity. Higher valence and arousal ratings for negative images during Test 1 predicted greater dream recall (valence: t(344)=2.05, p=0.04; arousal: t(327)=2.04, p=0.04). Additionally, the more negatively participants rated the images at Test 1, the more negative their dreams tended to be (t(166)=-2.11, p=0.04). Dream recall was linked to reduced next-morning emotional reactivity (valence: t(413)=-2.89, p=0.004; arousal: t(413)=-2.65, p=0.01), with stronger reductions following more negative dreams (β=0.15, t(182)=2.86, p=0.005). Conclusion Menstrual cycle phase influenced how dreams shaped overnight emotional memory. Negative waking experiences increased dream recall and shaped dream content—and recalling dreams, especially negative ones, reduced emotional reactivity and typically strengthened emotional memory—but this benefit disappeared in the late-luteal phase when there are declining reproductive hormones. These findings suggest a novel interaction between the menstrual cycle and dreaming, showing that hormonal fluctuations reshape how sleep and dreams regulate emotional experience and memory. Support (if any) RF1AG061355 (Baker/Mednick)
Abstract - The rapid expansion of digital content on the World Wide Web has made efficient information retrieval an increasingly complex challenge. To assist users in navigating this vast information landscape, several retrieval mechanisms have been developed. Among these, two broad strategies are commonly employed: structured index-based search and autonomous agent-driven search. This work presents an intelligent search agent that leverages a Genetic Algorithm (GA) to perform global web searches. The agent is implemented using the Java programming language and operates by evaluating multiple candidate documents to identify the most relevant result for a given input. The proposed system is built on the Java platform and integrates the Merriam-Webster lexical database to expand query terms with their semantic equivalents. Query words are tokenized, synonym tables are constructed, and varied term combinations are assembled and dispatched to a search engine parser. The returned web pages represent the most semantically aligned results for the original query. Key Words: Genetic Algorithm, web page retrival, searching.
Swallowing is tightly coupled with emotion-autonomic states, while olfactory pathways project to limbic and hypothalamic hubs that can enhance parasympathetic activity and reduce stress. Whether a pleasant natural-wood odor can modulate swallowing-related brain activity and behavior remains unclear. To test whether Hinoki (Japanese cypress) inhalation improves affective ratings and increases the repetitive saliva swallowing test (RSST) difference (post-pre; swallows/30 s), and whether it suppresses swallowing-related activity in the premotor cortex (Brodman area 6 (BA6)) and the hypothalamus. The effects of exploratory whole-brain and interaction between Task and Odor were also evaluated. In a randomized within-subject crossover with a one-week interval, participants inhaled Hinoki essential oil or odorless rice-oil control. We assessed brain activity during voluntary saliva swallowing using functional magnetic resonance imaging (fMRI). Outcomes included subjective ratings (odor intensity, preference, comfort, recollection) and RSST counts per 30 s; RSST difference was computed as post-pre inhalation. Hinoki improved affective ratings and increased RSST difference compared to control. The odor main effect showed reduced activation in three regions-bilateral BA6 and the hypothalamus. An interaction effect between Task (swallowing vs. rest) and Odor (Hinoki vs. control) was found in the right putamen. The pattern supports a model: odor-induced autonomic calming (lower heart rate, reduced hypothalamic activity) lowers premotor load and improves swallowing, reflected in RSST difference. These findings suggest that aromatherapy-assisted dysphagia rehabilitation using olfactory priming-particularly for patients with post-stroke sequelae or post-stroke depression with poor appetite-may help enhance training efficiency and recovery.
= 702), we examined whether the expectancy of an action effect shapes the affective evaluation of corresponding action-effect episodes. In each study, participants responded to stimuli by clicking on buttons that produced effects with either high (stimulus-congruent) or low (stimulus-incongruent) expectancy. Induced affect was assessed using both implicit (affective priming) and explicit (valence ratings) measures. Overall, high-expectancy episodes elicited relatively more positive affect than low-expectancy episodes. For our implicit measure, this effect persisted unless correspondence among all task events (stimulus, response, and effect) was simultaneously reduced (Experiments 2 + 3). Furthermore, this outcome could not be attributed to mere visual mismatch between stimulus and effect (Experiment 4) and was observed even when stimulus-congruent effects were less likely to occur than stimulus-incongruent ones (Experiments 5 + 6). These findings suggest that expected action outcomes elicit positive affect, thus offering a parsimonious explanation for performance advantages previously attributed to more complex mechanisms like ideomotor accounts or response monitoring. More broadly, we propose that affective evaluation of action-effect episodes represents a fundamental mechanism governing behavior across multiple psychological domains, from basic action control to complex behavior in various settings. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
This study addresses a fundamental question in music psychology: which specific, dynamic acoustic features predict human listeners' emotional responses along the dimensions of valence and arousal. Our primary objective was to develop and validate an interpretable computational model that can serve as a tool for testing and advancing theories of music cognition. Using the publicly available DEAM dataset, containing 1,802 music excerpts with continuous valence-arousal ratings, we developed a novel, theory-guided neural network. This proposed model integrates a convolutional pathway for local spectral analysis with a Transformer pathway for capturing long-range temporal dependencies. Critically, its learning process is constrained by established principles from music psychology to enhance its plausibility. A core finding from an analysis of the model's attention mechanisms was that distinct acoustic patterns drive the two emotional dimensions: rhythmic regularity and spectral flux emerged as strong predictors of arousal, whereas harmonic complexity and musical mode were key predictors of valence. To validate our analytical tool, we confirmed that the model significantly outperformed standard baselines in predictive accuracy, achieving a Concordance Correlation Coefficient (CCC) of 0.67 for valence and 0.73 for arousal. Furthermore, an ablation study demonstrated that the theory-guided constraints were essential for this superior performance. Together, these findings provide robust computational evidence for the distinct roles of temporal and spectral features in shaping emotional perception. This work demonstrates the utility of interpretable machine learning as a powerful methodology for testing and refining psychological theories of music and emotion.
This paper presents a small-scale dependency treebank for Tunisian Arabic (TADT) developed within the Universal Dependencies framework, addressing the scarcity of linguistic resources for the Arabic varieties.The approach employs domain adaptation, leveraging a machine learning model (UDPipe 1.0) trained on Algerian Arabic data to annotate 100 Tunisian Arabic social media comments, followed by manual correction.This pilot study evaluates the feasibility of using machine learning-assisted annotation to scale resource development for spoken Arabic and identifies key challenges in cross-dialectal transfer for improving annotation quality and efficiency.This work contributes to more inclusive and fair representation of Arabic linguistic varieties in academic research and NLP applications.
Language errors play a crucial role in language learning and teaching. Using Indonesian in ways that exceed its standard communicative and linguistic norms is considered improper. Such deviations indicate a misuse of the language, whether orally or in writing, as they do not adhere to the established communication parameters and grammatical structures. The objective of this investigation is to scrutinize linguistic inaccuracies present in in the book titled Kuliah Muhammadiyah Gerakan Tajdid. The book titled Kuliah Muhammadiyah Gerakan Tajdid: Pendekatan Ideologis, Historis, Organisatoris, dan Analisis was written by H. Anhar Anshori, M.S.I., Ph.D., and published by UAD Press. This book provides a comprehensive description of Muhammadiyah as an Islamic movement, the da'wah of enjoining good and forbidding evil, and tajdid (renewal). This book is often used as an important reference for the Muhammadiyah course at various Muhammadiyah universities. This research adopts a descriptive qualitative methodology, employing an observational approach. The findings indicate the presence of 12 distinct types of orthographic errors. These encompass issues related to letter usage (specifically, two errors involving capitalization), word formation (including four instances of non-standard or improperly formed words), and the incorrect application of punctuation marks (one error concerning the use of single quotation marks, three errors with hyphens, and one error in semicolon usage).
This study empirically analyzed the error-processing patterns of five Korean grammar correction tools (Naver, Daum, Bareun-Hangeul, Gemini 3, and ChatGPT 5.2) currently utilized in the writing environment as of 2026. The findings revealed that while conventional rule- and statistics-based systems were effective for orthographic corrections, they exhibited a technical plateau in deep sentence-level error processing. In contrast, Gemini 3, a Generative Large Language Model (LLM), demonstrated superior contextual understanding and sentence restructuring capabilities, achieving an accuracy rate exceeding 90% across all domains. However, beneath this performance leap, a new form of “technical deceptiveness”—characterized by grammatical hallucinations and post-hoc rationalizations where incorrect corrections are fluently justified—was identified. Based on these results, this study first proposes “Critical AI Literacy” education, which enables learners to understand the probabilistic nature of generative AI and question the authenticity of its suggestions. Second, it suggests a “Writer-led Verification Process” as a core direction for literacy education in the AI era, empowering writers to make final judgments by cross-referencing AI-generated texts with linguistic norms rather than being subordinated to technical convenience. This research holds pedagogical significance by providing an empirical foundation for learner-centered critical writing education through a comprehensive examination of the possibilities and limitations of LLMs.
The article is devoted to the study of the phenomenon of variation of foreign language vocabulary in modern Russian. The scientific problem lies in the need to rethink this linguistic phenomenon in the context of digital communication, where spontaneous spoken language becomes the main source of innovation, while traditional research is often limited to the analysis of normative vocabulary. The purpose of the work is a comprehensive analysis of the manifestations and mechanisms of variation based on the material of the live speech of users of social networks. To achieve this goal, a set of methods was used, including monitoring and recording oral speech in natural situations (podcasts, streams, audio messages), a continuous selection of examples from social networks (VK, Telegram, TikTok, Rutube) for the period 2023-2025, as well as contextual and distributional analysis to identify educational models and functions of options. As a result of the study, three main types of variation were identified and systematized: phonetic and graphic, morphological and semantic. It has been established that it is in the Internet environment that foreign language vocabulary maximizes its enriching potential, demonstrating deep assimilation through creative word-making. The practical significance of the work lies in the fact that its results can be used in compiling dictionaries of neologisms and in teaching Russian, as well as serve as a basis for further research on the dynamics of linguistic norms in the digital age. The prospects of the study are seen in tracking the long-term fate of the identified accidents and their possible transition to use.