Search Results (324)

L1 transfer is well-attested in SLA; negative transfer is common when learners encounter a typologically distinct language. English-speaking learners often struggle with Japanese passives, which differ significantly from English passives both conceptually and grammatically. While English passives primarily defocus the agent, Japanese passives serve multiple semantic and discourse functions, often maintaining a focus on (and empathy toward) the experiencer. This small study examines how conceptual understandings drawn from usage-based (UB) analyses influence the acquisition of Japanese passives. Using corpus studies and acquisition research as a foundation, we developed concept-based language instruction (C-BLI) integrating UB-focused concepts. Our analysis of students’ oral languaging, gesture, and story-writing data from an immediate post-test and two delayed (3 weeks and 6 months post-instruction) post-tests show individual differences and demonstrate how a UB-based C-BLI approach facilitated developmental processes in Japanese over time; students improved their grasp of concepts taught via multi-modal materials, including visual materializations of concepts and ocean wave gestures. Conceptual and linguistic development were evidenced via oral languaging and story-writing. The most frequently used passive verb was iu ‘say,’ which has been found to be often passivized in L1 speakers’ production and previous SLA research. Findings contribute to broader discussions of how conceptual restructuring may affect L2 acquisition of complex grammatical constructions. Full article

Automated compliance checking (ACC) integrated with Building Information Modeling (BIM) requires regulatory texts that can be translated into machine-executable rules. Existing studies have largely focused on rule extraction techniques and ontology-based modeling within single jurisdictions, leaving the upstream question of regulatory readiness underexplored. This study introduces a clause-level framework for assessing the formalizability of building regulations and applies it to four documents covering accessibility and fire safety in the United Kingdom and Kazakhstan. The corpus was decomposed into 2361 enforceable clauses, classified using a ten-category semantic taxonomy, and evaluated against four formalizability criteria: explicit scope, measurable requirement, deterministic outcome, and design-stage data availability. Clauses were classified as formalizable only when satisfying all four criteria simultaneously. UK documents reached 85% formalizability for accessibility and 90% for fire safety, compared with 77% and 51% for the corresponding Kazakh standards. The largest gap was observed in fire safety, where the Kazakh corpus contained fewer BIM-oriented and spatially explicit checks and a higher share of clauses lacking evidential specification. The proposed framework supports clause-level diagnosis of regulatory automation readiness, and a four-stage roadmap links linguistic structure to digital maturity in both jurisdictions. Full article

The rapid proliferation of online misinformation poses significant risks to democratic processes and public decision-making. However, existing machine learning and deep learning approaches often rely on large annotated datasets and exhibit limited robustness under severe class imbalance and low-resource conditions, particularly in Spanish-language contexts. To address this, this study proposes an interpretable and robust framework for misinformation detection under such constraints. A unified, linguistically grounded and data-centric pipeline is developed, integrating structured lexical, syntactic, and semantic features with synthetic minority augmentation, class-balanced ensemble learning, autoencoder-based representation learning, and active learning under data scarcity. Importantly, the framework systematically evaluates the interaction between these components within a reproducible experimental setting. Results demonstrate that the proposed approach achieves consistent improvements in macro-averaged F1 and minority-class recall compared to baseline models, while reducing performance variance across folds. Ensemble and augmentation strategies provide the most stable configurations, enhancing the detection of underrepresented classes. Moreover, the use of interpretable linguistic features allows predictions to be associated with discourse-level patterns, improving transparency. Consequently, the framework offers a reproducible, computationally efficient, and interpretable solution for misinformation detection in low-resource environments, supporting practical deployment and future multilingual extensions. Importantly, this study provides the first systematic analysis of the interaction between linguistic representations and imbalance mitigation strategies under extreme data scarcity. Full article

Background/Objectives: This study describes the development and validation of the Danish Sentence Test (DAST), a Danish-language, adaptive speech-in-noise test constructed from a linguistically balanced corpus using a template-based method. This approach enables controlled linguistic variation while maintaining lexical consistency and may serve as a model for developing similar speech materials in other languages. Methods: Sentences spoken by one female talker from the DAST corpus were sorted into 44 balanced lists of 20 sentences using a psychometric optimization procedure. Speech reception thresholds (SRTs) were measured in 20 normal-hearing participants using headphone playback with speech-shaped noise. Results: Across the 44 sentence lists, the mean SRT was −5.3 dB SNR, with list means within ±0.5 dB of the grand average under the tested configuration. The average within-subject standard deviation was 0.7 dB, and the grand-average psychometric slope was 18.5%/dB. A statistically significant within-session training effect of approximately 0.02 dB per measurement. Conclusions: This study provides normative speech reception threshold (SRT) data for the adaptive Danish Sentence Test (DAST) in normal-hearing listeners under a defined headphone-based speech-in-noise paradigm and demonstrates that the resulting sentence lists yield comparable performance across lists. The template-based construction and optimization approach offers a framework for developing linguistically rich sentence-in-noise tests in other languages. Full article

We investigate the linguistic complexity and emotional valence of popular song lyrics across English ($n=1491$), Spanish ($n=307$), and German ($n=225$), using an analytical corpus of 2023 tracks drawn from 2113 deduplicated tracks on Spotify’s weekly Top 200 charts (2019–2021). Transformer-based sentiment analysis is combined with complexity-science tools to characterize both the affective content and the structural organization of commercially successful lyrics. A multilingual BERT model reveals a mild negative skew across all three languages (63.7% negative overall); the 1.003-point English–German gap observed under the English-centric VADER lexicon collapses to 0.127 points under BERT, indicating that earlier cross-linguistic sentiment differences are largely measurement artifacts. Word frequency distributions follow Zipf’s law in all three languages ($R^2>0.96$), with English steepest ($\alpha =1.409$) and German shallowest ($\alpha =1.181$). Detrended fluctuation analysis indicates persistent long-range correlations ($H\approx 0.66$–$0.76$; none of the 50 shuffled surrogates exceeded the observed values), and multifractal singularity spectra are statistically indistinguishable across languages once corpus size is controlled (all pairwise Mann–Whitney $p>0.13$). Streaming counts within the Top 200 are concentrated (German Gini $=0.556$) but, given the truncated single-snapshot sample, are reported as within-chart descriptors rather than population-level scaling. Full article

The focus of our contribution is on lexical Austriacisms, i.e., lexical features of the Austrian standard language. Whereas in previous studies, only a small set of Austriacisms has been examined, with food terms being particularly popular, this contribution considers 76 lexical variables with 205 variants (Austriacisms and their co-variants), which are examined through complex variationist corpus analyses. The data is provided by the Austrian Media Corpus (amc), which represents the language use of the Austrian print media landscape in the 21st century. The analyses are both (short-term) diachronic and synchronic, taking into account the variation in vivo. Irrespective of the frequency-based “starting point” of a variant at the beginning of the 21st century, its relative frequency remains at comparable levels in the course of the observation period. Contrary to the threat scenarios of previous studies, our corpus analyses indicate the relative stability of the majority of Austriacisms over the 23 years studied (2001–2023). Full article

This work presents an innovative approach to test the Arabic language proficiency assessment via Automatic Speech Recognition (ASR) by enhancing the proficiency of the Whisper model in transcribing Arabic speech. The core of our research involved fine-tuning the Whisper model using a substantial, large-scale Arabic speech corpus, with a specific focus on Modern Standard Arabic. This process used a 2000-h Arabic-labeled speech corpus, the QASR dataset, and improved the model’s Word Error Rate (WER). After optimization, the fine-tuned Whisper model’s WER was reduced from 35% to 7% on the QASR dataset, corresponding to an absolute reduction of 28 percentage points (approximately 80% relative reduction). These results demonstrate the strong generalization ability of the fine-tuned model across multiple Arabic ASR benchmarks. A key component of our methodology was the development of a sophisticated scoring system. This system integrates various similarity metrics, such as cosine similarity, the Jaccard index, and the Levenshtein distance, with a machine learning regression model. This multifaceted system provides a comprehensive assessment of reading proficiency, proposing a practical automated assessment method that contributes to the field of AI language transcription and to its application in the assessment of students’ reading. Our research also introduces the ICONET dataset, an augmented Arabic speech corpus comprising 3160 h of diverse and tailored audio–text pairs designed for fine-tuning ASR models. This study demonstrates the potential of fine-tuning pretrained models for specific linguistic contexts (Arabic), establishing a foundation for future research in ASR and language technology. Full article

Large language models (LLMs) are increasingly investigated for abstract screening in systematic reviews, yet it remains unclear whether screening errors attributed to linguistic complexity arise from intrinsic semantic sensitivity or from its interaction with decision architecture. We examined how five polarity variants of logically equivalent eligibility criteria—affirmative inclusion, antonymic exclusion, predicate negation, verb-level negation, and double negation—affect screening outcomes in a controlled biomedical task. Using 1000 abstracts from a reconstructed Cochrane review corpus (50 TARGET; 950 non-targets), we implemented four abstract-visible criteria within a sequential hard-gated pipeline, where failure at any step triggered irreversible exclusion. Under hard gating, linguistic polarity alone produced substantial and statistically significant variation in recall. For GPT-5.1, recall ranged from 0.72 to 0.32 despite identical logical predicates and input data. Replication with GPT-3.5 Turbo yielded a similar divergence (0.92–0.18), confirming generalization across model generations. TARGET losses were concentrated at criteria typically satisfied but inconsistently reported in abstracts, indicating conservative exclusion under evidential under-specification. To assess whether this effect is semantic or architectural, we reimplemented screening using a scoring-based evidence-accumulation framework in which each criterion contributed graded support and inclusion was determined by a tunable threshold. Under scoring, recall increased across all variants and converged within a high-sensitivity regime, while residual polarity effects were attenuated but remained detectable. Linguistic differences shifted from structural recall collapse to controlled precision–recall trade-offs. These findings show that negation sensitivity is strongly mediated by decision architecture. Irreversible gating amplifies local uncertainty into false-negative exclusion, whereas cumulative scoring preserves uncertainty and enables controllable operating thresholds. Full article

Preserving ancient languages is essential for understanding the cultural and linguistic heritage of humanity. Old English, however, remains critically under-resourced, which limits its accessibility to modern natural language processing (NLP) techniques. We present a scalable framework that uses advanced large language models (LLMs) to generate high-quality Old English texts to address this gap. In this study, we specifically employ state-of-the-art models, including Llama-3.1-8B and Mistral-7B, as our foundation models, which are then adapted to the unique characteristics of Old English. Our approach combines parameter-efficient fine-tuning (Low-Rank Adaptation (LoRA)), data augmentation via back-translation, and a dual-agent pipeline that separates content generation (in English) and translation (into Old English). Evaluation with automated metrics (BLEU, METEOR, and CHRF) shows improvements over baseline models, with BLEU scores increasing from 26 to over 65 for English-to-Old English translation. Expert human assessment confirms high grammatical accuracy and stylistic fidelity in the generated texts, with average scores of 9.0/10 for inflection and word order, 9.1/10 for lexical authenticity, and 7.8 for semantic coherence. These results demonstrate that the framework can reliably expand limited historical corpora while maintaining linguistic integrity, with immediate practical applications in digital humanities research, computational philology, and the development of educational resources for Old English study. Beyond expanding the Old English corpus, our method offers a practical blueprint for revitalizing other endangered languages, thus linking AI innovation with the goals of cultural preservation. Full article

The concept of verbal periphrasis has historically been a controversial one in Romance linguistics, especially in the Hispanic context, where there has been disagreement as to what multiverbal constructions should be considered periphrastic. One of the points of contention has been the class of infinitive causatives. This article revisits the controversy by focusing on Spanish “enviar/mandar a + infinitive” structures and drawing on historical corpus data. The analysis of various examples leads to the conclusion that strictly periphrastic instances of this constructional class are present across all main stages of the history of Spanish. Additionally, a series of quantitative analyses reveals what appear to be two distinct grammaticalization processes and a degrammaticalization process. These findings are discussed in connection with broader themes in the field, such as syntactic ambiguity and the concept of analyzability. Full article

This study investigates how YouTube food travel vloggers semiotically construct destination images and potentially function as informal culinary ambassadors through gastrodiplomacy mechanisms, using Uzbekistan as a case study of emerging tourism markets. Although digital content creators are increasingly recognised as shaping tourism flows, a systematic understanding of the multimodal semiotic mechanisms through which food travel vlogs construct destination meanings remains limited. Using multimodal discourse analysis, this study examines six YouTube food travel videos on Uzbekistan (over 28 million combined views) from two prominent creators. The analysis integrates Kress and van Leeuwen’s visual grammar, Halliday’s systemic functional linguistics, van Leeuwen’s sound semiotics, and Norris’s multimodal interaction analysis to code a 60-segment corpus. Comparative analysis reveals 25 notable differences in semiotic features between the two creators, identifying two distinct semiotic profiles. Vlogger 1 primarily follows a parasocial intimacy model marked by direct gaze (89.2%), frequent second-person address (78.4%), and comparatively minimal editing. In contrast, Vlogger 2 adopts a cinematic documentary model characterised by first-person narration (56.5%), constructed visuals (60.9%), and gastronomic heritage narratives (34.8%). Despite these divergences, shared conventions centred on food composition, upbeat music, positive evaluation, and sharing gestures indicate a stable semiotic grammar of food travel vlogging. Analysis further reveals that orientalist dynamics and resistance to orientalism coexist within the same representational practice phenomenon termed ‘layered orientalism’, with distinct implications for how emerging destinations are mediated to international audiences. These findings suggest that digital content creators may employ distinct semiotic strategies that could function as informal culinary ambassadors through gastrodiplomacy mechanisms, potentially constructing destination awareness and cultural meaning for international audiences. This study contributes to theory on multimodal destination image construction and offers implications for how emerging tourism destinations might leverage multi-creator strategies to build culturally grounded destination brands. Full article

Loanword Research on Anglicisms has largely centered on lexical borrowing and phonological adaptation with comparatively limited attention to morphosyntactic integration in recipient grammars. This study examines the morphosyntactic behavior of 74 single-word Anglicisms—monosyllabic structures with monophthongal vowels—drawn from phonetically classified corpora of spontaneous Mexican Spanish produced by Spanish–English bilinguals in the Tijuana–San Diego border region. Building on prior acoustic analyses based on F1 and F2 vowel measurements, the study investigates the relationship between phonological adaptation and morphosyntactic integration. Results reveal a gradient pattern of incorporation. Anglicisms exhibiting Spanish-like phonetic properties tend to occupy canonical syntactic positions and show greater compatibility with Spanish functional morphology, whereas phonetically non-adapted forms more frequently resist morphological marking and display island-like behavior within otherwise Spanish clauses. The analysis examines distribution across nominal, adjectival, and prepositional domains and object positions to assess morphosyntactic integration degrees. The former is illustrated as follows: (1) guardo cash ([kaʃ]) por si acaso; (2) si hacen match ([mæʧ]), puede funcionar. Adopting a usage-based and contact-oriented perspective for syntactic borrowing, the study is situated within the Matrix Language Frame model and recent approaches to insertional borrowing. A central contribution lies in establishing a principled link between morphosyntactic behavior and an independently motivated phonetic classification, offering convergent evidence for the systematic integration of Anglicisms into Spanish grammar. At a broader analytical level, the study advances debates on syntactic borrowing and contact-induced change by demonstrating that Anglicisms are subject to Spanish morphosyntactic constraints rather than functioning as unconstrained lexical insertions, and by developing an interface-based account of borrowing that captures the gradient nature of grammatical incorporation in contact settings and contributes a corpus-based, empirically grounded perspective to typologies of borrowing in Spanish contact linguistics. Full article

This article offers a theoretically nuanced and empirically grounded investigation into the paradoxical afterlife of classical versification within the poetic practices of the Russian and Soviet avant-garde. Challenging the persistent historiographic narrative that equates avant-garde poetics with an unequivocal rupture from tradition, the study demonstrates that canonical metrical forms—most notably iambic tetrameter—continued to operate as structurally productive, albeit critically reconfigured, elements within experimental verse. Drawing on a broad corpus encompassing poetic manifestos, verse texts, and prose writings by Vladimir Maiakovskii, Ilia Sel’vinskii, Semen Kirsanov, and Nikolai Aseev, the authors combine close formal analysis with quantitative prosodic modeling, including linguistic and speech models derived from Kolmogorov–Taranovsky verse theory. The article argues that avant-garde poets did not simply negate inherited metrics but subjected them to a process of internal recomposition, shifting attention from meter as a fixed scheme to rhythm as a dynamic, semantically charged construct. While rhythmic innovation is shown to be consciously engineered in verse, the analysis of verse-like fragments in prose reveals persistent, unconscious attachments to “classical” rhythmic patterns, particularly the Pushkinian alternating rhythm. This tension between declarative rejection and latent continuity illuminates the avant-garde’s distinctive mode of negotiating tradition: not abolishing it, but instrumentalizing it within a broader project of total artistic reorganization. The study thus reframes avant-garde prosody as a site where innovation and inheritance coexist in a state of productive contradiction, reshaping our understanding of modernist poetic technique. Full article

The 2021–2024 global energy crisis intensified the energy transition, with mainstream media coverage playing a pivotal role in shaping public perceptions. Guided by Burke’s and Lippmann’s theories, and supported by corpus-based critical and rhetorical discourse analyses, this interdisciplinary study aimed to analyze the role of lexical patterns and rhetorical strategies in framing the transition within a corpus of 1341 news articles retrieved from the websites of five English-language mainstream media outlets. Corpus-based analysis identified generic frames, including economic consequences, responsibility, conflict, technological, emotion, and moral duty frames. Rhetorical discourse analysis revealed specific frames, including economic opportunities, technological progress and challenges, energy security and independence, global leadership, energy partnerships, partisan divide, global disparities, corporate greenwashing, necessity, hope, and uncertainty frames, that indicated an ambivalence in the framing of the transition, thereby contributing to the polarization and manipulation of public opinion. The findings indicated a discrepancy: while British, American, and Brazilian media focused more on political divides, Indian and Chinese media emphasized energy partnerships and patriotism. Appeals to experts were less frequent, whereas appeals to emotions were often employed to shape public perceptions. The findings illustrate how lexical patterns and rhetorical strategies function as powerful framing tools within journalism, applied linguistics, and media rhetoric. Full article

Navigating extensive legislative corpora is often impeded by the linguistic complexity inherent in legal texts. To address this, we present a novel topic representation learning method designed to facilitate the systematic exploration of legislative content. We demonstrate the efficacy of this approach by applying it to the vast corpus of Italian legislation comprising about 74 k laws with more than 300 k articles. While current topic models group documents by latent semantic similarity, they often lack the granularity required for precise navigation. Our approach augments these representations by integrating our topic modeling framework with multi-label profiles. We enrich the representation of individual laws by extracting and ranking the top 10 keywords based on their relevance to the enclosing topic, subsequently aggregating these rankings to construct a comprehensive, alternative description of the broader legal themes. By bridging latent semantic clusters with explicit, LLM-generated labels, this method yields a highly interpretable representation of the corpus, significantly enhancing the profiling and navigability of complex legislative content. We improve over our baseline representation in 74.67% of cases, showing potential for re-use in highly specialized text corpora. Full article