Search Results (101)

This paper investigates the morphosyntactic and semanto-pragmatic behavior of the German neo-pronoun xier, a gender-neutral form used to refer to nonbinary individuals. Framed within the Minimalist Program, the analysis explores how xier carries a gender feature that encodes nonbinary identity—not through binary morphological marking, but via presupposition. The use of xier triggers a presupposition about the referent’s identity: that they are nonbinary. This gender feature is not absent, void or underspecified, but interpretively rich and categorically distinct. The analysis thus rejects any account treating xier as lacking gender. Instead, it argues that xier exemplifies a grammatical strategy of encoding gender beyond the binary, through formal structures that engage the interpretive system directly. The paper further argues that xier’s morphosyntactic profile—including its compatibility with standard agreement morphology—shows that nonbinary gender can be syntactically represented and participate fully in φ-feature interactions. Drawing on cross-linguistic comparisons (e.g., English they and the Italian adaptation ze), the study shows how presuppositional gender encoding supports stable φ-Agree, interface-compatible labeling without requiring binary valuation. The proposal refines the architecture of φ-features by allowing for interpretively active gender categories that are formally encoded even when they do not match traditional binary specifications. This account offers a model for how minimalist syntax can accommodate socially driven innovations without abandoning core theoretical principles. Xier, in this light, demonstrates that grammatical systems can expand to encode emerging reference categories—not by omitting gender, but by formally encoding nonbinary gender via presupposition. This study is the first to offer a formal syntactic account of a German neo-pronoun, linking socially driven innovation to core φ-feature operations like Agree and valuation. Full article

This study examines Xuanzang’s methodology for translating the Yogācārabhūmi into Chinese, with particular focus on his translation of passages explaining the central concept of volition (cetanā). Through comparative analysis of Chinese and Tibetan translations—particularly passages for which Sanskrit parallels are not available—this paper investigates textual divergences and interpretative challenges in the two translations. Comprehensive examination of textual evidence across the Yogācārabhūmi corpus confirms that a problematic term in Xuanzang’s Chinese translation—suiyu—authentically reflects the Sanskrit source text, specifically corresponding to the Sanskrit term anupradāna. This allows us greater insight into Xuanzang’s translational strategy and its reception among his disciples. While previous scholarship has traditionally emphasized Xuanzang’s strict fidelity to Sanskrit grammatical structures, this study reveals a more sophisticated approach: he employed suiyu as a translation of anupradāna specifically for technical discussions of consciousness and mental factors, but adopted more idiomatic renderings of anupradāna in general contexts. However, the interpretations of suiyu among his disciples suggest that even this careful methodology sometimes failed to achieve its intended clarity, highlighting the inherent tension between preserving original textual features and ensuring accurate semantic transmission—a fundamental challenge in cross-cultural Buddhist transmission that continues to shape our understanding of Buddhist traditions. Full article

With the widespread application of Internet of Things (IoT) devices, the security of embedded systems faces severe challenges. As an embedded operating system widely used in critical mission scenarios, the security of the TCP stack in VxWorks directly affects system reliability. However, existing protocol fuzzing methods based on network communication struggle to adapt to the complex state machine and grammatical rules of the TCP. Additionally, the lack of a runtime feedback mechanism for closed-source VxWorks systems leads to low testing efficiency. This paper proposes the vxTcpFuzzer framework, which generates structured test cases by integrating the field features of the TCP. Innovatively, it uses the memory data changes of VxWorks network protocol processing tasks as a coverage metric and combines a dual anomaly detection mechanism (WDB detection and heartbeat detection) to achieve precise anomaly capture. We conducted experimental evaluations on three VxWorks system devices, where vxTcpFuzzer successfully triggered multiple potential vulnerabilities, verifying the framework’s effectiveness. Compared with three existing classic fuzzing schemes, vxTcpFuzzer demonstrates significant advantages in test case acceptance rates (44.94–54.92%) and test system abnormal rates (23.79–34.70%) across the three VxWorks devices. The study confirms that protocol feature fusion and memory feedback mechanisms can effectively enhance the depth and efficiency of protocol fuzzing for VxWorks systems. Furthermore, this approach offers a practical and effective solution for uncovering TCP vulnerabilities in black-box environments. Full article

Verbal nouns in Insular Celtic languages have long been a subject of interest because they are capable of exhibiting both nominal and verbal properties, posing a persistent challenge when it comes to determining their precise categorization. This study therefore seeks to examine the intersective gradience of verbal nouns in Scottish Gaelic from a functional-typological and multidimensional perspective, providing an insight into the interaction between their morphosyntactic, semantic, and pragmatic properties and their lexical categorization, and, consequently, encouraging a broader discussion on linguistic gradience. This hybrid category plays a central role in the clause structure of Scottish Gaelic, as it appears in a wide range of distinct grammatical constructions. Drawing on a range of diagnostic tests revealing the morphosyntactic and semantic properties of verbal nouns across various contexts (e.g., etymology, morphological structure, inflection, case marking, TAM features, syntactic function, types of modification, form and position of objects, distributional patterns, cleft constructions, argument structure, subcategorization, etc.), this line of research identifies two key environments, depending on whether the construction features a verbal noun functioning either as a verb or a noun. This distinction aims to illustrate the way in which these contexts condition the gradience of verbal nouns. By doing so, it provides strong evidence for their function along a continuum ranging from fully verbal to fully nominal depending on their syntactic context and semantic and pragmatic interpretation. In conclusion, the findings of this study suggest that the use of verbal nouns blurs the line between two lexical categories, often displaying mixed properties that challenge a rigid categorization. Full article

Sign language avatar systems have emerged as a promising solution to bridge communication gaps where human sign language interpreters are unavailable. However, the design of these avatars often fails to account for the diversity in how users acquire and perceive sign language. This study presents a rapid review of 17 empirical studies (2020–2025) to synthesize how linguistic and cognitive variability affects sign language perception and how these findings can guide avatar development. We extracted and synthesized key constructs, participant profiles, and capture techniques relevant to avatar fidelity. This review finds that delayed exposure to sign language is consistently linked to persistent challenges in syntactic processing, classifier use, and avatar comprehension. In contrast, early-exposed signers demonstrate more robust parsing and greater tolerance of perceptual irregularities. Key perceptual features, such as smooth transitions between signs, expressive facial cues for grammatical clarity, and consistent spatial placement of referents, emerge as critical for intelligibility, particularly for late learners. These findings highlight the importance of participatory design and user-centered validation in advancing accessible, culturally responsive human–computer interaction through next-generation avatar systems. Full article

In some languages, prosodic cues on word stems can be used to predict upcoming suffixes. Previous studies have shown that second language (L2) users can process such cues predictively in their L2 from approximately intermediate proficiency. This ability may depend on the mapping of the L2 prosody onto first language (L1) perceptual and functional prosodic categories. Taking as an example the Danish stød, a complex prosodic cue, we investigate an acquisition context of a predictive cue where L2 users are unfamiliar with both its perceptual correlates and its functionality. This differs from previous studies on predictive prosodic cues in Swedish and Spanish, where L2 users were only unfamiliar with either the perceptual make-up or functionality of the cue. In a speeded number judgement task, L2 users of Danish with German as their L1 (N = 39) and L1 users of Danish (N = 40) listened to noun stems with a prosodic feature (stød or non-stød) that either matched or mismatched the inflectional suffix (singular vs. plural). While L1 users efficiently utilised stød predictively for rapid and accurate grammatical processing, L2 users showed no such behaviour. These findings underscore the importance of mapping between L1 and L2 prosodic categories in second language acquisition. Full article

Currently, few-shot setting and entity nesting are two major challenges in named-entity recognition (NER). Compared to English, Chinese NER not only has issues such as complex grammatical structures, polysemy, and entity nesting but also faces low-resource scenarios in specific domains due to difficulties in sample annotation. To address these two issues, we propose a Chinese few-shot named-entity recognition model that integrates multi-label prompts and boundary information (MPBCNER). This model is an improvement based on a pre-trained language model (PLM) combined with a pointer network. First, the model uses multiple entity label words and position slots as prompt information in the entity recognition training task. Activating the relevant parameters in PLM associated with the corresponding entity labels through the prompt information improved the model’s performance in entity recognition under small-sample data. Secondly, by using a Graph Attention Network (GAT) to construct the boundary information extraction module, we integrated boundary information with text features, allowing the model to pay more attention to features near the boundaries when recognizing entities, thereby improving the accuracy of entity boundary recognition. Experiments on multiple public small-sample datasets and our own annotated datasets in the field of government auditing demonstrated the effectiveness of this model. Full article

Text classification remains a challenging task in natural language processing (NLP) due to linguistic complexity and data imbalance. This study proposes a hybrid approach that integrates grammar-based feature engineering with deep learning and transformer models to enhance classification performance. A dataset of factoid and non-factoid questions, further categorized into causal, choice, confirmation, hypothetical, and list types, is used to evaluate several models, including CNNs, BiLSTMs, MLPs, BERT, DistilBERT, Electra, and GPT-2. Grammatical and domain-specific features are explicitly extracted and leveraged to improve multi-class classification. To address class imbalance, the SMOTE algorithm is applied, significantly boosting the recall and F1-score for minority classes. Experimental results show that DistilBERT achieves the highest binary classification accuracy, equal to 94%, while BiLSTM and CNN outperform transformers in multi-class settings, reaching up to 92% accuracy. These findings confirm that grammar-based features provide critical syntactic and semantic insights, enhancing model robustness and interpretability beyond conventional embeddings. Full article

Children appear to acquire new words effortlessly from complex auditory input. However, this process is highly intricate, requiring the simultaneous integration of phonetic and phonemic details, prosodic cues, and grammatical structures. Furthermore, different components of a language’s sound system—such as phonemes, syllables, and prosodic features—appear with different frequencies in the input and follow distinct patterns of distribution in speech. This article reviews research that illustrates how infants’ growing understanding of their native language sound system facilitates their acquisition of new words. Full article

Despite significant advances in deep learning-based image captioning, many state-of-the-art models still struggle to balance visual grounding (i.e., accurate object and scene descriptions) with linguistic coherence (i.e., grammatical fluency and appropriate use of non-visual tokens such as articles and prepositions). To address these limitations, we propose a hybrid image captioning framework that integrates handcrafted and deep visual features. Specifically, we combine local descriptors—Scale-Invariant Feature Transform (SIFT) and Bag of Features (BoF)—with high-level semantic features extracted using ResNet50. This dual representation captures both fine-grained spatial details and contextual semantics. The decoder employs Bahdanau attention refined with an Attention-on-Attention (AoA) mechanism to optimize visual-textual alignment, while GloVe embeddings and a GRU-based sequence model ensure fluent language generation. The proposed system is trained on 200,000 image-caption pairs from the MS COCO train2014 dataset and evaluated on 50,000 held-out MS COCO pairs plus the Flickr8K benchmark. Our model achieves a CIDEr score of 128.3 and a SPICE score of 29.24, reflecting clear improvements over baselines in both semantic precision—particularly for spatial relationships—and grammatical fluency. These results validate that combining classical computer vision techniques with modern attention mechanisms yields more interpretable and linguistically precise captions, addressing key limitations in neural caption generation. Full article

This article focuses on the right edge of nominal stems in Greek and aims to show that stem-final segments should be analyzed as distinct morphological constituents. Two types of such constituents are identified. On the one hand, stem endings such as -a(ð), -i(ð), and -a(t) have a predictable distribution, as they are found in nouns with specific morphosyntactic properties and stress patterns. On the other hand, stem endings like -o, -a, and -i cannot function as predictors of the morphosyntactic status of the noun, although they may convey information about its stress position. The distinction between the two constituent categories is captured through an analysis couched within Distributed Morphology. Specifically, it is proposed that stem endings of the first category function as nominalizer exponents, while those of the second category serve as exponents of a Theme node, which is inserted post-syntactically and bears no grammatical features. The allomorphic variation exhibited by these exponents is accounted for by means of a phonological analysis based on Gradient Harmonic Grammar. The proposed approach is shown to capture empirical generalizations that have been overlooked in traditional grammatical descriptions and theoretical analyses based on multiple stem allomorphs. Full article

As the prevalence of machine-written texts grows, it has become increasingly important to distinguish between human- and machine-generated content, especially when such texts are not explicitly labeled. Current artificial intelligence (AI) detection methods primarily focus on human-like characteristics, such as emotionality and subjectivity. However, these features can be easily modified through AI humanization, which involves altering word choice. In contrast, altering the underlying grammar without affecting the conveyed information is considerably more challenging. Thus, the grammatical characteristics of a text can be used as additional indicators of its origin. To address this, we employ a newly developed rule-based parser to analyze the grammatical structures in human- and machine-written texts. Our findings reveal systematic grammatical differences between human- and machine-written texts, providing a reliable criterion for the determination of the text origin. We further examine the stability of this criterion in the context of AI humanization and translation to other languages. Full article

The Tujia ethnic group is one of the major ethnic groups in China, with a long history and abundant cultural heritage. As a distinctive architectural style, Tujia dwellings have evolved over thousands of years, developing a wealth of construction techniques and embodying the wisdom of local craftsmen. These construction techniques are a valuable asset of Tujia folk dwellings but still rely on the oral tradition among craftsmen. Therefore, it is extremely valuable for enriching the world’s architectural system and heritage inheritance to refine these techniques and transform them into regularized digital properties. The “L”-shaped system of Tujia houses is the most common type of Tujia house, featuring both the main house and the wing house, and can distinctly represent the construction technology and style characteristics of Tujia houses. The grammar of “L”-shaped houses is the core part of the grammar of Tujia houses and is also important for analyzing and inheriting the construction technology of Tujia houses. Shape grammar is an analytical method centered on the refinement of rules. This paper takes advantage of its ability to analyze and refine rules, and based on the rich Tujia architectural material library, it summarizes the corpus and refines the grammatical rules of “Generation of the main structure framework”, “Roof truss conversion and support”, “Side houses and stilted structures”, and “Cantilevered elements and corners” into four dimensions, along with many detailed grammars. These rules are transformed into a programming language and parameterized toolkit, providing a detailed summary of the construction logic and techniques. Ultimately, an “L”-shaped construction grammar for Tujia traditional dwellings has been proposed, and with the help of software tools such as Grasshopper, the digital regeneration has been completed. Full article

Kazakh Sign Language (KSL) is a crucial communication tool for individuals with hearing and speech impairments. Deep learning, particularly Transformer models, offers a promising approach to improving accessibility in education and communication. This study analyzes the syntactic structure of KSL, identifying its unique grammatical features and deviations from spoken Kazakh. A custom parser was developed to convert Kazakh text into KSL glosses, enabling the creation of a large-scale parallel corpus. Using this resource, a Transformer-based machine translation model was trained, achieving high translation accuracy and demonstrating the feasibility of this approach for enhancing communication accessibility. The research highlights key challenges in sign language processing, such as the limited availability of annotated data. Future work directions include the integration of video data and the adoption of more comprehensive evaluation metrics. This paper presents a methodology for constructing a parallel corpus through gloss annotations, contributing to advancements in sign language translation technology. Full article

The research on aspect-based sentiment analysis (ABSA) mostly relies on a single attention mechanism or grammatical semantic information, which makes it less effective in dealing with complex language structures. To address the challenges in fine-grained sentiment analysis tasks, this paper establishes a novel model of syntax and semantics based on feature fusion together with a graph convolutional network (SSFF-GCN), which includes a dual-channel information extraction layer by combining syntactic dependency graphs and semantic information, and consists of three important modules: the syntactic feature enhancement module, semantic feature extraction module, and feature fusion module. In the grammar feature enhancement module, this model uses dependency trees to capture the structural relationship between emotional words and target words and adds a dual affine attention module to enhance grammar learning ability. In the semantic feature extraction module, aspect-aware attention combined with self-attention is used to extract semantic associations in sentences, which ensures effective capture of long-distance dependency information. The feature fusion module dynamically combines the enhanced syntactic and semantic information through a gated mechanism; therefore, it enhances the model’s ability to express emotional features. The empirical results show that the SSFF-GCN model is generally superior to existing models on several publicly available datasets. Full article