Search Results (78)

The processing difficulty of subject relative clauses (SRCs) and object relative clauses (ORCs) in Mandarin Chinese has been a controversial issue in psycholinguistics. Memory-based accounts and experience-based accounts make contrastive predictions regarding the processing asymmetry. Given that older adults tend to have lower memory and richer language experience, the processing of relative clauses in older adults can reveal which theoretical account offers a more adequate explanation of sentence comprehension. The present study compared the processing of Mandarin SRCs and ORCs in older and younger adults using a self-paced reading paradigm. The results revealed that both groups showed lower accuracy for SRCs than ORCs, but this effect was larger in older adults. These age-related differences cannot be attributed to different strategic trade-off mechanisms and task experience. During online processing, older adults performed more slowly than younger adults, but no significant interaction was found between age and RC type. These findings suggest that aging affects sentence comprehension at the post-interpretive and decision processes, while online syntactic processing remains relatively preserved but globally slowed. The findings are largely consistent with memory-based accounts, indicating that age-related changes in sentence comprehension may be attributed to memory decline in older adults. Full article

Syntactic complexity is a crucial aspect of assessing writing quality in Chinese as a second language. While existing literature predominantly focuses on synchronic features of syntactic complexity, particularly changes in complexity indices, less attention has been paid to its diachronic development and interactions among these indices. Drawing upon Dynamic Systems Theory, this exploratory longitudinal study traces the developmental trajectories of syntactic complexity indices and their interactions in CSL writings of 15 native Cambodian speakers within a single instructional context. The main results are as follows: (i) The syntactic complexity indices exhibited fluctuating and nonlinear growth. Among them, the length of topic chain clauses showed notable variation (range: 1.12 to 9.05), while relatively small changes (range: 0.4 to 2.39) occurred in the number of topic chain clauses. (ii) The development trends in the number of topic chains and the number of zero components showed no significant difference (p = 0.086, Cohen’s d = 0.31). In contrast, the development trends in the number of topic chain clauses and the length of topic chain clauses differed significantly (p = 0.039, Cohen’s d = 0.65). (iii) Individual differences in syntactic complexity were observed among learners in similar learning environments. These findings provide a detailed, context-bound description of the dynamic and complex syntactic development observed in the Chinese writing of 15 participants. The study highlights the value of employing multiple perspectives to capture such complexity and underscores the need for future research with more diverse samples and designs to test the generalizability of these patterns. Full article

Early diagnosis of cognitive decline is vital for timely treatment of mild cognitive impairment (MCI) and Alzheimer’s disease (AD), yet standard clinical assessments often miss subtle longitudinal language changes. We propose a hierarchical hybrid intelligence framework integrating long-context language modeling, temporal progression, semantic graph reasoning, psycholinguistic biomarkers, and contrastive progression learning to classify patient states (Normal, MCI, AD) from longitudinal electronic health record (EHR) notes. The model was trained on 4500 patients and 68,000 clinical notes from Medical Information Mart for Intensive Care III (MIMIC-III) and externally validated on the Medical Information Mart for Intensive Care IV (MIMIC-IV) clinical notes dataset (5200 patients, 72,000 notes). Inputs combined Biomedical and Clinical Bidirectional Encoder Representations from Transformers (BioClinicalBERT) embeddings, Bidirectional Long Short-Term Memory (Bi-LSTM) temporal encodings, Graph Sample and Aggregate (GraphSAGE)-based Unified Medical Language System (UMLS) concept graphs, and psycholinguistic vectors (lexical diversity, grammatical complexity, discourse coherence). On the MIMIC-III hold-out set, the model achieved 99.999% accuracy, a macro F1-score of 0.999, a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 0.999, and a temporal stability variance of 0.0008. Monte Carlo cross-validation (10,000 folds) yielded $99.997\pm 0.003$% accuracy and $0.999\pm 0.001$ macro F1. Feature ablation confirmed distinct gains from temporal, semantic, and psycholinguistic modules, improving performance by 1.1% over text-only baselines. Cross-cohort zero-shot testing on MIMIC-IV showed strong generalization with minimal decline in macro F1 and balanced accuracy. Explainability analyses, such as SHapley Additive exPlanations (SHAP) token/concept attribution, attention maps, counterfactual perturbations, and psycholinguistic importance, revealed clinically interpretable markers, such as pronoun overuse, reduced lexical diversity, and syntactic simplification, as predictors of decline. Our framework supports scalable, non-invasive early screening in a variety of healthcare settings by providing longitudinally stable predictions. Full article

The processes of teaching and learning are primarily humanistic. However, contemporary artificial intelligence (AI) technology has significantly changed these processes. The current qualitative study aimed to explore this phenomenon by investigating the role that chatbots can play as language tutors in improving ESL students’ writing. Specifically, the study investigated students’ perceptions and experiences to assess the influence of ChatGPT-generated written communication on ESL writing improvement. Data were collected through semi-structured interviews with undergraduates from the College of Languages and Translation at a public university in Riyadh, Saudi Arabia. The emerging themes revealed that students held positive perceptions of the chatbot as a tutor, highlighting that collaborative learning with the chatbot facilitated the acquisition of writing skills and increased engagement in the writing process. Findings also showed noticeable improvement in language development, at lexical, syntactic, semantic, and pragmatic levels, as well as in the use of cognitive and metacognitive writing strategies. The study recommends reevaluating traditional writing instruction methodologies and highlights the benefits of integrating AI chatbots into second-language writing pedagogy. Furthermore, the study emphasises students’ need for accessible English-language tutoring, such as chatbots, which provide immediate, real-time writing instruction. The study also addresses the implications of incorporating AI-powered chatbots into writing curricula at Saudi universities. Full article

With the increasing use of modern technologies in interior design, numerous recent studies have made the effects of kinetic-based design techniques on users’ perceptions a crucial topic, and sustainable performance has emerged as essential. From this standpoint, this study uses a space syntax approach to investigate how human behavioral performance in workspaces is affected by kinetic interiors. Three kinetic-based design strategies were employed to evaluate changes in spatial configuration characteristics, and the relevant terminology was adapted to account for the use of kinetic technology. The paper adopts a comparative analysis model to follow these changes using four syntactic measures: integration, choice, connectivity, and clustering coefficient. The proposed evaluation approach is applied to a traditional office building in Port Said, Egypt, showcasing various aspects of kinetic technology in workspaces. The study’s findings elucidate the correlations between design strategies and the resulting spatial characteristics, guiding designers in evaluating the features of each system and facilitating comparisons between them. Finally, the study’s main aim is to propose a three-step design process as a guideline for creating an integrated kinetic technology design, involving the evaluation of the proposed alternatives to achieve the desired spatial characteristics. Full article

This study investigates how second language (L2) learners acquire morphologically complex English words, focusing on affixal neutrality—whether suffixes preserve the phonological form and semantic transparency of the base (e.g., -ness in happiness) or trigger phonological/orthographic changes (e.g., -ity in activity). Drawing on linguistic theories of morphological decomposition and lexical representation, we examine how this property influences different dimensions of derivational knowledge. Fifty-four Mandarin-speaking secondary school EFL learners completed three receptive tasks targeting relational knowledge (morphological relatedness), syntactic knowledge (category awareness), and distributional knowledge (contextual appropriateness). Lexical items varied in affixal neutrality, and participants’ accuracy and response times were analysed across three L2 proficiency levels. Affixal neutrality significantly affected performance in the relational knowledge task, with neutral suffixes facilitating accuracy and faster responses. Effects were attenuated in syntactic and distributional tasks, suggesting domain-specific sensitivity to neutrality. L2 Proficiency was associated with higher accuracy across all three domains but did not substantially affect processing speed. These findings highlight the selective role of a theoretically motivated morphological property in L2 lexical acquisition and show how linguistic concepts such as affixal neutrality can form the basis of targeted hypotheses, bridging theoretical linguistics and empirical research in second language learning. Full article

Amidst global concerns for linguistic diversity, this systematic review synthesizes six decades (1965–2025) of research on Tanka, a critically endangered language spoken by the boat people along Southeast China. Analyzing 42 studies identified through the PRISMA framework, the review reveals significant sociolinguistic and epistemological imbalances. Research output disproportionately focuses on phonetics and phonology (50%), while neglecting grammar, lexicon, and sociolinguistic vitality. Linguistically, Tanka demonstrates substantial contact-induced convergence with Cantonese or Pinghua within multilingual ecologies; nevertheless, it retains distinctive phonological shifts, a unique maritime lexicon, and grammatical innovations, reflecting both regional alignment and endogenous community practices. Its heterogeneous genetic affiliation highlights local sociohistorical contact dynamics. Rapid intergenerational language shift is documented across communities, driven by intersecting pressures, including state-led urbanization, Mandarin-centric education policies, demographic shifts, occupational change, and enduring social stigmatization. Therefore, community attitudes often prioritize socio-economic mobility through dominant languages over heritage maintenance. Persistent gaps include limited syntactic and discourse analysis, minimal use of quantitative and computational methods (e.g., AI-assisted documentation), insufficient geographic coverage, and a lack of longitudinal shift studies. The field thus urgently requires enhanced international engagement via English publications and a decisive shift towards collaborative, community-centered revitalization frameworks that address power asymmetries and harness cultural resilience. Full article

Paraphrasing is the process of transforming a given text into another text using alternative lexical or syntactic forms while preserving its original meaning. Paraphrasing significantly affects several Natural Language Processing (NLP) applications, such as machine translation (MT) and data augmentation. Paraphrasing suffers from a specifically designed metric, and most research adopts metrics developed for other NLP purposes. Paraphrase evaluation remains challenging due to the limitations of surface-level similarity metrics such as BLEU and ROUGE. Therefore, this research aims to develop a new metric for paraphrase generation, the Comprehensive Paraphrasing Evaluation Score (CPES). Furthermore, the CPES requires lexical language resources; thus, the research uses an Arabic corpus and produces a new Arabic lexical dictionary (Rabih dictionary). The CPES considers major paraphrasing criteria, including sentence structure, changes in word forms, synonym substitution, and paraphrased-sentence lexical diversity (LD). Each CPES supports interpretability by enabling decomposition into the criterion that drives the final result. The research finds that (1) the CPES effectively measures the modification ratio between original and paraphrased sentences, and (2) the text category impacts the CPESs. Full article

This article concerns the morpheme a in Czech. It occurs in nominals, conjunctions, and various verbal predicates. In contrast to the common practice of treating such a exponents as independent, accidentally homophonous elements, it is argued that some of these as can be treated as one item. What the syncretic as have in common is pluralizing semantics. Thus, the article proposes that verbal number (specifically, plurality) is related to nominal number and conjunctions. The article addresses the questions of how the multifunctionality of morphemes—such as the Czech a—can be analyzed and which tools of lexical–realizational approaches to morphology are most suitable for the analysis. In addition to the plural interpretation, a brings about changes in the argument structure of verbal predicates and fulfills several functions in the nominal and conjunction domains. The analysis is couched in the Distributed Morphology framework. However, contrary to expectations, the multifunctional a is not treated as an underspecified marker. It is analyzed as an overspecified marker that can realize (i.e., span) several syntactic heads: the pluralization head with the pluralization operator, the voice head, plus some other heads present in verbs and nominals. It is argued that the best option for deriving the multifunctional property of a is to assume the superset principle and pre-linearization spanning. Full article

Objectives: Although behavioral studies suggest that syntactic comprehension is relatively preserved in healthy aging, the underlying neural mechanisms remain a subject of intense debate. This review aims to synthesize neuroimaging and electrophysiological evidence to clarify how the aging brain reorganizes to maintain language resilience. Methods: A systematic search was conducted across multiple databases such as PubMed and Web of Science. Twenty-three relevant empirical studies meeting our inclusion criteria were identified. The synthesis focused on regional activation patterns, functional connectivity, and temporal dynamics during syntactic processing in older adults compared to younger controls. Results: The review revealed four key findings. First, the core left-lateralized frontotemporal language network remains resilient during syntactic processing in older adults. Second, age-related changes in functional connectivity within the core network are heterogeneous, with evidence for both reduction and preservation. Third, right-hemisphere homologues are increasingly recruited, but its functional significance is condition-dependent, serving both compensatory and non-compensatory roles. Fourth, older adults increasingly engage domain-general cognitive control regions, such as the dorsolateral prefrontal cortex and pre-supplementary motor area, to support syntactic processing under high cognitive loads. Conclusions: On the basis of these findings, we propose the Graded Compensation and Cognitive Scaffolding (GCCS) model which posits that language resilience is maintained through a graded and condition-dependent adaptation of neural resources. This study critically evaluates the current literature and highlights the need for more methodologically rigorous studies to better understand the effects of aging on syntactic processing and its neural basis. Given the limited number of eligible studies, the findings of this review should be interpreted with caution. More well-powered, longitudinal research is needed to uncover the trajectory of neural reorganization during syntactic processing in older adults. Full article

Background: Rapid Eye Movement (REM) sleep behavior disorder (RBD) is a parasomnia resulting from degeneration of pontine and medullary circuits responsible for muscle atonia during REM sleep, leading to dream-enactment behaviors and vocalizations. It is strongly linked to α-synucleinopathies, particularly Parkinson’s disease. Current biomarkers such as neurophysiological measures and imaging support diagnosis and monitoring, but remain invasive or costly. Aim: This study aims to evaluate vocal and speech alterations as exploratory, non-validated candidate biomarkers of REM sleep behavior disorder. Methods: A systematic review was conducted according to PRISMA 2020 guidelines. PubMed, IEEE Digital Library Web of Science, Embase and the Cochrane Library were systematically searched for studies published from database inception to November 2025, as preregistered on the Open Science Framework. Studies were selected through a multi-step screening process and underwent qualitative quality assessment. Results: Twelve studies met inclusion criteria. Individuals with RBD exhibited abnormal nocturnal vocalizations and early lexical, syntactic, and narrative disruptions despite preserved perceptual speech. Quantitative analyses identified consistent deficits in prosody, phonation stability, timing, and articulation, with significant group differences and diagnostic accuracy up to 96% sensitivity. Multilingual cohorts demonstrated progression over time, while digital phenotyping detected emerging Parkinsonian signs with AUC > 0.70. Conclusions: Speech and vocal abnormalities in iRBD reflect early neurodegenerative changes and show promising but still exploratory diagnostic and prognostic potential. Integrating vocal markers with established biomarkers may enhance early detection; however, further research is required to validate a reliable and reproducible vocal signature of prodromal synucleinopathies. Full article

We developed an AI-assisted zero-trust control system at low capital expenditure to retrofit brownfield Ethernet environments without disruptive hardware upgrades or costly software-defined networking migration. Legacy network infrastructures in small and medium-sized enterprises (SMEs) lack the flexibility and programmability required by modern zero-trust architectures, creating a persistent security gap between static Layer-1 deployments and dynamic cyber threats. The developed system addresses this gap through a modular architecture that integrates genetic-algorithm-based virtual local area network (VLAN) optimization, large language model-guided firewall rule synthesis, threat-intelligence-driven policy automation, and telemetry-triggered adaptive isolation. Network assets are enumerated and evaluated through a risk-aware clustering model to enable micro-segmentation that aligns with the principle of least privilege. Optimized segmentation outputs are translated into pfSense firewall policies through structured prompt engineering and dual-stage validation, ensuring syntactic correctness and semantic consistency. A retrieval-augmented generation pipeline connects live telemetry with historical vulnerability intelligence, enabling rapid policy adjustments and automated containment responses. The system operates as an overlay on existing managed switches, orchestrating configuration changes through standards-compliant interfaces such as simple network management protocol and network configuration protocol. Experimental evaluation in a representative SME testbed demonstrates substantial improvements in segmentation granularity, refining seven flat subnets into thirty-four purpose-specific VLANs. Compliance scores improved significantly, with the International Organization for Standardization/International Electrotechnical Commission 27001 rising from 62.3 to 94.7% and the National Institute of Standards and Technology Cybersecurity Framework alignment increasing from 58.9 to 91.2%. All 851 automatically generated firewall rules passed dual-agent validation, ensuring reliable enforcement and enhanced auditability. The results indicate that the system developed provides an operationally feasible pathway for legacy networks to achieve zero-trust segmentation with minimal cost and disruption. Future extensions will explore adaptive learning mechanisms and hybrid cloud support to further enhance scalability and contextual responsiveness. Full article

Decoder-only language models (LMs) store factual knowledge directly in their parameters, resulting in large model sizes, costly retraining when facts change, and limited controllability in knowledge-intensive information systems. These models frequently mix stored knowledge with user-provided context, which leads to hallucinations and reduces reliability. To address these limitations, we propose KIT (Knowledge-Injected Transformer), a modular encoder–decoder architecture that separates syntactic competence from factual knowledge representation. In KIT, the decoder is pre-trained on knowledge-agnostic narrative corpora to learn language structure, while the encoder is trained independently to compress structured facts into compact latent representations. During joint training, the decoder learns to decompress these representations and generate accurate, fact-grounded responses. The modular design provides three key benefits: (1) factual knowledge can be updated by retraining only the encoder, without modifying decoder weights; (2) strict domain boundaries can be enforced, the modular design provides a structural foundation for reducing knowledge source confusion and hallucinations, with its actual effectiveness awaiting future validation on standard hallucination benchmarks; and (3) interpretability is improved because each generated token can be traced back to encoder activations. A real-world experimental evaluation demonstrates that KIT achieves competitive answer accuracy while offering superior controllability and substantially lower update costs compared to decoder-only baselines. These results indicate that modular encoder–decoder architectures represent a promising and reliable alternative for explainable, adaptable, and domain-specific question answering in modern information systems. Full article

This study examines lexical and syntactic convergence between Dai Lue and Chinese in the multilingual environment of Sipsongpanna, employing an apparent-time approach across three generational cohorts (N = 90, balanced gender). Through mixed-methods analysis (structured questionnaires and semi-structured interviews), significant diachronic variation was observed. Younger speakers exhibited pronounced convergence, adopting Chinese-derived syntactic patterns (e.g., prenominal quantifiers and preverbal adjunct phrases) and borrowing Chinese lexical elements (e.g., an adverb sɛn⁵⁵ ‘first’ ← Chinese 先 xiān, and a superlative marker tsui³⁵ ‘most/best’ ← Chinese 最 zuì). Middle-aged speakers use transitional hybrid structures, while older speakers more consistently maintain native Dai Lue features. The results conform with Labov’s age-grading model in contact linguistics and refine Thomason’s borrowing hierarchy by revealing two factors: First, the prestige of the Chinese language drives convergence among youth. Second, syntactic compatibility with Chinese is mediated not merely by language structure, but by discourse-pragmatic needs, functional load redistribution, and the social indexicality of borrowed structures. This underscores the interplay between sociolinguistic motivations and structural-adaptive constraints in language change. The findings provide critical insights into language contact mechanisms among ethnic minorities of China, with implications for sociolinguistic theory, language revitalization efforts, and bilingual education policy implementation in linguistically diverse communities. Full article

Two theories that align with and support Usage-based approaches to language acquisition are Functionalism, which motivates the communicative functions of form-meaning connections produced by grammatical phenomena, and Connectionism, which provides a biologically-plausible framework for understanding language processes. An essential part of the learning process for second language (L2) learners is to understand how the target language differs in the ways it represents similar functionality, as well as functions not represented in learners’ first languages (L1s). In some cases, communicative functions served by the L1(s) are mirrored by similar-enough processes in the L2, so that the L1 processes can be utilized by the L2 system by entrenched L1 pathways. However, other communicative functions must develop their own processing pathways to accommodate differing L2 structures, because certain grammatical features allow for, or force particular ways of processing information. If the L2 learner does not notice and adopt the L2 processes needed for distinct linguistic structures, L1 processes connected to similar meanings will continue to be utilized. As a case in point, this paper outlines why L1 English learners of German as an L2 must change the ways they process syntactic role assignment away from syntactic cues towards ones embedded in morphology and morphosyntax. The goal of this paper is to explain how Functional and Connectionist theories, housed within a larger Usage-Based understanding of Second Language Acquisition, can account for frequently unsuccessfully or only partially acquired L2 German case marking, and why instructional interventions like Concept-Based Language Instruction and Processing Instruction all produce uptake of L2 German case marking to varying degrees. Full article