Search Results (200)

Robust and transferable approaches for evaluating research capacity—whose measurable expression is reflected in research output—are essential for evidence-based science policy and strategic research management. This study develops an integrated framework to assess global scholarly capacity and regional disparities by combining semantic-similarity-based literature filtering, bibliometric mapping, dynamic performance assessment, and spatial analytical techniques into a coherent and replicable model. A Sentence-BERT model ensures thematic precision and dataset consistency, while CiteSpace 6.1.R3 is used tomap publication trajectories, thematic evolution, and influential contributors. A dynamically weighted TOPSIS model incorporates temporal variation to quantify national research capacity, and spatial analyses—including gravity center analysis, Theil index decomposition, spatial autocorrelation, gray relational analysis, and the Geographical Detector Model—identify disparity patterns and their explanatory associations. Applied to urban ecosystem security research (2001–2023), an emerging interdisciplinary field within sustainability science, the framework shows that China and the United States dominate research output, whereas European journals exert strong academic influence. The field has advanced through three stages, with increasing emphasis on ecosystem services and sustainable development. GDP, environmental pressure, and urbanization rate show the strongest explanatory associations with research capacity, and interactive effects—especially those involving GDP—exceed single-factor explanatory strength. Ecological baseline conditions such as NDVI and climate exhibit only limited associations, functioning mainly as contextual factors. Policy implications highlight four priorities: strengthening interdisciplinary and cross-regional collaboration in developing regions; promoting equity-oriented research agendas in developed regions; establishing unified definitions and validated evaluation frameworks; and advancing dynamic, systems-based approaches to ecosystem security analysis. By shifting attention from ecological status assessment to the dynamics of scientific knowledge production and research capacity, this study advances methodological foundations for research evaluation and enriches analytical approaches in urban ecosystem security, offering a generalizable framework for identifying capacity differences and supporting evidence-informed policy design. Full article

Child-directed speech (CDS) is characterized by a suite of exaggerated acoustic features, with elevated fundamental frequency (pitch) being a prominent and widely adopted component. While caregivers and educators frequently use high-pitch speech with young children, its perceptual preference among preschool-aged children, particularly in tonal languages like Mandarin, remains empirically unclear. This study aimed to investigate Mandarin-speaking preschoolers’ explicit preferences for manipulated pitch levels at the sentence frame while also examining the potential influence of lexical tone. Ninety-four children aged 3–6 years completed a binary forced-choice preference task. They listened to sentences systematically varying in three pitch levels (high, normal, low F₀) and five tone conditions (the four Mandarin lexical tones and a mixed-tone condition), with other acoustic parameters controlled. Results revealed that children showed no significant preference for high-pitch over normal-pitch speech. However, they exhibited a strong aversion to low-pitch speech. Furthermore, children’s pitch-level preferences were not modulated by the lexical tone of the sentences. These findings clarify that Mandarin-speaking preschoolers do not inherently prefer the high pitch typical of CDS over a normal speaking voice but are distinctly unfavorable toward low pitch. The study suggests that effective, listener-centered communication in early childhood settings may prioritize avoiding unusually low pitch rather than deliberately raising pitch, offering evidence-based guidance for pedagogical practice and adult–child interaction. Full article

This work proposes a hybrid Uzbek sentiment analysis model (sometimes referred to as tonality analysis in the local literature) that integrates contextual text representations with named-entity information from an NER module and emoji-based emotional cues that are common in short online messages. To provide a comprehensive baseline comparison, we evaluate seven approaches—SVM, LSTM, mBERT, XLM-RoBERTa-base, mDeBERTa-v3, LaBSE, and the proposed hybrid model—covering both classical machine learning and modern multilingual transformer architectures for low-resource sentiment tasks. The overall pipeline begins with Uzbek-specific text normalization to reduce noise from informal spellings, transliteration variants, and inconsistent apostrophe usage. In parallel, the system performs explicit emoji extraction to capture affective signals that are often expressed non-verbally in social media texts. Next, we construct three complementary feature streams: a context encoder for sentence-level semantics, NER-driven entity features that encode entity mentions and types, and an emotion module that models emoji priors and their interaction with contextual meaning. These streams are fused into a unified representation and fed to a final classifier to predict sentiment polarity. Experiments on an Uzbek test set demonstrate that the hybrid model reaches an F1-score of 0.92, consistently outperforming text-only baselines. The results indicate that entity-aware and emoji-informed features improve robustness under sarcasm/irony, mixed sentiment with multiple targets, and orthographic noise, making the approach suitable for social media analytics, public opinion monitoring, customer feedback triage, and recommendation-oriented text mining. Full article

Transformer-based models face persistent challenges in long-document summarization due to fixed input-length constraints. Hybrid approaches address this limitation by applying extractive preprocessing to select salient sentences for downstream abstractive summarization. However, many unsupervised extractive methods, including TextRank and LexRank, rely on heuristic graph centrality and often struggle to preserve semantic coherence or control redundancy. This paper proposes GravRank, an unsupervised and deterministic extractive summarization framework that models sentence importance as an emergent property of pairwise semantic interactions governed by a softened Plummer potential. Sentences are embedded in a shared semantic space, and a global energy function is defined over all sentence pairs using a softened interaction kernel. This formulation jointly encodes relevance and redundancy within a single scoring function, avoiding iterative graph propagation, supervised training, and post hoc diversity filtering. The deterministic extractive output is used as input to a BART-based abstractive summarization model, forming a hybrid pipeline for long and semantically dense documents. Experiments on the BillSum, PubMed, and GovReport datasets show that GravRank improves over classical unsupervised baselines, remains competitive with recent extractive methods, and yields a competitive result in downstream abstractive summarization when combined with BART. Full article

A persistent challenge for Deaf and Hard-of-Hearing individuals is the communication gap between sign language users and the hearing community, particularly in regions with limited automated translation resources. In Saudi Arabia, this gap is amplified by the reliance on Saudi Sign Language (SSL) and the scarcity of real-time, sentence-level translation systems. This paper presents a real-time system for sentence-level recognition of continuous SSL and direct mapping to natural spoken Arabic. The proposed system operates end-to-end on live video streams or pre-recorded content, extracting spatio-temporal landmark features using the MediaPipe Holistic framework. For classification, the input feature vector consists of 225 features derived from hand and body pose landmarks. These features are processed by a Bidirectional Long Short-Term Memory (BiLSTM) network trained on the ArabSign (ArSL) dataset to perform direct sentence-level classification over a vocabulary of 50 continuous Arabic sign language sentences, supported by an idle-based segmentation mechanism that enables natural, uninterrupted signing. Experimental evaluation demonstrates robust generalization: under a Leave-One-Signer-Out (LOSO) cross-validation protocol, the model attains a mean sentence-level accuracy of 94.2%, outperforming the fixed signer-independent split baseline of 92.07%, while maintaining real-time performance suitable for interactive use. To enhance linguistic fluency, an optional post-recognition refinement stage is incorporated using a large language model (LLM), followed by text-to-speech synthesis to produce audible Arabic output; this refinement operates strictly as post-processing and is not included in the reported recognition accuracy metrics. The results demonstrate that direct sentence-level modeling, combined with landmark-based feature extraction and real-time segmentation, provides an effective and practical solution for continuous SSL sentence recognition in real-time. Full article

This article investigates how cognitive and grammatical mechanisms shape variable singular–plural agreement in Spanish perception–verb constructions, a domain where speakers alternate between agreement with the postverbal NP₂ and agreement with the infinitival complement. Building on usage-based and cognitive linguistics approaches, this study examines whether factors related to perceptual modality and conceptual salience underlie these alternations. A corpus analysis of pronominal infinitive constructions with ver and oír reveals divergent patterns across modalities, with visual perception favoring plural agreement and auditory perception favoring singular agreement. To evaluate whether these tendencies reflect deeper linguistic preferences, an acceptability-rating task systematically manipulated modality, agreement, and animacy. The results show no overall interaction between modality and agreement, but they identify a robust effect of animacy: sentences with human referents received higher ratings than those with inanimate referents. Moreover, animacy modulated the influence of modality and agreement in opposite directions, suggesting that speakers’ evaluations are sensitive to the ontological nature of the perceived stimulus. Together, the findings show that agreement variation reflects flexible conceptual construal and that corpus and experimental evidence offer complementary insights into the interface between morphosyntax, perception and salience in Spanish. Full article

This study investigates how argument structure interacts with Information Structure (IS) in Dative Experiencer (DE) psych-verbs of the piacere/gustar type in Italian and Spanish. These verbs display non-canonical mapping between thematic and grammatical roles, in which the Experiencer surfaces as a dative object and the Theme as the subject. Through a semi-spontaneous production experiment based on the Question with a Delayed Answer (QDA) methodology, the study elicited natural utterances to investigate how speakers encode Information Focus (IF) on the Theme. The results show a consistent pattern across the two languages, with a strong preference for postverbal realizations of the Theme and frequent overt expression of the Experiencer, interpreted as a Familiar Topic. Preliminary prosodic data further support this interpretation, showing that the Experiencer bears a low tonal contour typical of given material, whereas the postverbal subject has included in the prosodic boundary of the sentence. Taken together, these findings suggest that DE psych-verbs encode a grammar-internal mechanism that links thematic and informational hierarchies, where morphosyntactic structure, case, position and prosody jointly contribute to the interpretability of discourse relations. Full article

Down syndrome (DS) is associated with persistent language impairments that extend beyond early childhood, yet evidence from agglutinative languages remains limited. While morphosyntactic weaknesses have been well-documented in Indo-European languages, less is known about how such difficulties are manifested in Turkish, a language in which grammatical relations are primarily marked through morphology. In addition, short-term memory (STM) limitations, particularly in verbal domains, are characteristic of DS and may contribute to language outcomes. This study examined the interaction between morphosyntax and STM in Turkish-speaking children and adolescents with DS. A cross-sectional observational design was employed, including 12 monolingual Turkish-speaking participants with DS (aged 6;7–15;11) and 10 TD peers matched on nonverbal mental age. Participants completed standardized assessments of syntax and morphology, spontaneous language sampling, and STM tasks assessing verbal and visual memory. Children with DS performed significantly below controls on syntactic comprehension and production as well as morphological measures, with larger effects observed for syntax. Noun morphology was less accurate than verb morphology, likely reflecting increased morphophonological complexity. Regression analyses indicated that auditory digit span predicted sentence comprehension, whereas nonword repetition predicted morphological production indexed by mean length of utterance in morphemes. Substantial inter-individual variability was observed within the DS group. These findings suggest that morphosyntactic outcomes in Turkish-speaking children with DS are closely linked to verbal STM capacities and vary considerably across individuals, underscoring the importance of integrated assessment and individualized intervention planning. Future research with larger samples is warranted to confirm and extend these preliminary findings. Findings should be interpreted cautiously due to the limited sample size and are presented as preliminary descriptive evidence. This study provides initial data on Turkish-speaking individuals with Down syndrome. Full article

Extracting structured threat intelligence from unstructured cybersecurity texts requires accurate identification of entities together with their underlying semantic relations. However, threat reports often exhibit intricate sentence structures, long-range contextual dependencies, and tightly coupled entity–relation patterns, which pose substantial challenges for existing extraction approaches. To address these challenges, this study investigates joint entity–relation extraction from the perspective of semantic dependency modeling and develops HDIM-JER, a unified framework that captures structured interactions among heterogeneous linguistic features. HDIM-JER integrates character-level cues, contextual representations, and higher-order semantic dependency evidence to enhance structural awareness during joint inference, where different second-order dependency configurations provide an interpretable perspective on structurally symmetric and hierarchically asymmetric interaction patterns among entity–relation instances. By incorporating multi-level dependency interactions, HDIM-JER effectively alleviates error propagation associated with pipeline-based architectures and improves the modeling of complex relational dependencies. Extensive experiments on a threat intelligence corpus and a public benchmark dataset demonstrate consistent performance improvements over representative state-of-the-art methods in both entity recognition and relation extraction, confirming the effectiveness of higher-order semantic dependency interaction modeling for threat intelligence analysis. Full article

With the rapid development of intelligent computing and artificial intelligence, there is an increasing demand for efficient, interpretable, and interactive frameworks for fine-grained text analysis. In the field of argument mining, existing approaches are often constrained by sentence-level processing, limited exploitation of key linguistic markers, and a lack of human–AI collaborative mechanisms, which restrict both recognition accuracy and computational efficiency. To address these challenges, this paper proposes KFF-Transformer, a computing-oriented human–AI collaborative framework for fine-grained argument element identification based on Toulmin’s model. The framework first employs an automatic key marker mining algorithm to expand a seed set of expert-labeled linguistic cues, significantly enhancing coverage and diversity. It then employs a lightweight deep learning architecture that combines BERT for contextual token encoding with a BiLSTM network enhanced by an attention mechanism to perform word-level classification of the six Toulmin elements. This approach leverages enriched key markers as critical features, enhancing both accuracy and interpretability. It should be noted that while our framework leverages BERT—a Transformer-based encoder—for contextual representation, the core sequence labeling module is based on BiLSTM and does not implement a standard Transformer block. Furthermore, a human-in-the-loop interaction mechanism is embedded to support real-time user correction and adaptive system refinement, improving robustness and practical usability. Experiments conducted on a dataset of 180 English argumentative essays demonstrate that KFF-Transformer identifies key markers in 1145 sentences and achieves an accuracy of 72.2% and an F1-score of 66.7%, outperforming a strong baseline by 3.7% and 2.8%, respectively. Moreover, the framework reduces processing time by 18.9% on CPU and achieves near-real-time performance of approximately 3.3 s on GPU. These results validate that KFF-Transformer effectively integrates linguistically grounded reasoning, efficient deep learning, and interactive design, providing a scalable and trustworthy solution for intelligent argument analysis in real-world educational applications. Full article

Word segmentation is a fundamental component of lexical processing, and Chinese reading—lacking inter-word spacing—requires readers to identify word boundaries based on prior experience. Previous studies have shown that contextual predictability facilitates lexical identification in Chinese reading; however, its influence on word segmentation remains unclear. This study used eye-tracking to examine the relationship between contextual predictability and readers’ segmentation preferences during Chinese sentence reading. Overlapping ambiguous three-character strings (e.g., 花生长) were used as the region of interest (ROI), and a 2 (segmentation type: AB-C (e.g., 花生/长) vs. A-BC (e.g., 花/生长)) × 2 (contextual predictability: high vs. low) within-subjects design was adopted. A total of 76 native Chinese speakers completed the task. The results showed that contextual predictability had a significant effect on skipping probability: Highly predictable target character strings were skipped more often than low-predictability words. However, contextual predictability did not influence any eye-movement measure. In contrast, segmentation type produced consistent effects across all measures, with shorter reading times for AB-C than for A-BC, indicating a stable preference for two-character segmentation. More importantly, no interaction emerged between contextual predictability and segmentation type, and Bayesian model comparison further supported this conclusion. These findings suggest that Chinese reading involves a robust preference for AB-C segmentation and that contextual predictability and word segmentation operate as independent processes, with predictability exerting minimal influence on word segmentation during reading. This result supports the Chinese Reading Model (CRM). Full article

Aspect Sentiment Triplet Extraction (ASTE) aims to jointly identify aspect terms, opinion terms, and their associated sentiment polarities. Existing approaches, such as tagging or span-based modeling, often struggle with complex aspect–opinion interactions and long-distance dependencies. We propose a Knowledge-Augmented Graph Convolutional Network (KMG-GCN) that represents a sentence as a multi-channel graph integrating syntactic dependencies, part-of-speech tags, and positional relations. An adjacency tensor is constructed via a biaffine attention mechanism, while a multi-anchor triplet learning strategy with orthogonal projection enhances representation disentanglement. Furthermore, a pairwise refinement module explicitly models aspect–opinion associations, improving robustness against overlapping triplets. Experiments on multiple benchmarks demonstrate that KMG-GCN achieves state-of-the-art performance with improved efficiency and generalization. Full article

Text-to-video retrieval offers an intelligent solution for Internet of Vehicles (IoV) users to access desired content on demand. However, the constrained communication channels in IoV, characterized by low signal-to-noise ratios (SNR), pose significant obstacles to retrieval performance. To tackle these issues, this study presents SemTVR, a semantic communication framework dedicated to achieving superior robustness in text-to-video retrieval tasks in low-SNR IoV environments. By integrating the semantic communication paradigm with edge–cloud collaboration, our architecture leverages roadside unit (RSU) features and cloud resources to enable collaborative retrieval. We introduce a multi-semantic interactive reliable transmission mechanism that utilizes historical search records to enhance semantic recovery accuracy under adverse channel conditions. Furthermore, we devise a cross-modal fine-grained matching strategy assigning differentiated weights to video content and query sentences. Experimental results conducted on authoritative datasets demonstrate that SemTVR significantly outperforms baseline methods in terms of search accuracy, particularly in low SNR scenarios, validating its effectiveness for future IoV applications. Full article

This entry introduces an integrated model of reading that situates the Lexical Quality Hypothesis (LQH) within the Reading Systems Framework (RSF). The LQH posits that skilled reading depends on high-quality lexical representations—precise and flexible mappings of orthographic, phonological, morpho-syntactic, and semantic features—stored in the mental lexicon. These representations facilitate automatic word identification, accurate meaning retrieval, and efficient word-to-text integration (WTI), forming the foundation of text comprehension. Extending this micro-level perspective, the RSF positions lexical quality (LQ) within a macro-level cognitive architecture where the lexicon bridges word identification and reading comprehension systems. The RSF integrates multiple knowledge systems (linguistic, orthographic, and general world knowledge) with higher-order processes (sentence parsing, inference generation, comprehension monitoring, and situation model construction), emphasizing the bidirectional interactions between lower-level lexical knowledge and higher-order text comprehension. Central to this model is WTI, a dynamic mechanism through which lexical representations are incrementally incorporated into a coherent mental model of the text. This integrated model carries important implications for theory refinement, empirical investigation, and evidence-based instructional practices. Full article

Natural language processing is the technology used to interact with computers using human languages. An overlapping technology is Information Retrieval (IR), in which a user searches for the demanded or required documents from among a number of documents that are already stored. The required document is retrieved according to the relevance of the query of the user, and the results are presented in descending order. Many of the languages have their own IR systems, whereas a dedicated IR system for Sindhi still needs attention. Various approaches to effective information retrieval have been proposed. As Sindhi is an old language with a rich history and literature, it needs IR. For the development of Sindhi IR, a document database is required so that the documents can be retrieved accordingly. Many Sindhi documents were identified and collected from various sources, such as books, journal, magazines, and newspapers. These documents were identified as having potential for use in indexing and other forms of processing. Probabilistic modeling and pattern discovery were used to find patterns and for effective retrieval and relevancy. The results for Sindhi Information Retrieval systems are promising and presented more than 90% relevancy. The time elapsed was recorded as ranging from 0.2 to 4.8 s for a single word and 4.6 s with a Sindhi sentence, with the same starting time of 0.2 s. The IR system for Sindhi can be fine-tuned and utilized for other languages with the same characteristics, which adopt Arabic script. Full article