Syntactic Relations Research Articles

КАТЕГОРИЯ ПРИНАДЛЕЖНОСТИ И ПОСЕССИВНОЕ МЫШЛЕНИЕ ДРЕВНИХ ТЮРКОВ (на материале рунических памятников письменности)

The proposed article substantiates the possessive thinking of the ancient Turks and explores the category of belonging in various texts of the Old Turkic language, formed by synthetic morphological means, that is, through possessive affixes. On the basis of various researches of scientists, the authors indicate the relevance of the subject of possessive constructions in the ancient Turkic language, as affixes of belonging have “inalienable” properties of belonging, inherent only to the Turkic worldview of the most ancient times. Despite the extensive research on possessive forms, there are types of possessiveness that require more detailed study. This is necessary to enrich theoretical 27Том 3, No2, 2024 Язык и литература: теория и практикаISSN 2957-5567 (Print) 2957-5575 (Online)and historical data on the main pathways of formation and reasons for their emergence, grammatical methods of their formation, and their use as a means of expressing syntactic relationships. The authors have set the goal of the research to identify the peculiarities of possessive thinking among ancient Turks, as reflected in possessive affixes, and to examine how both the person and number of the subject of “inalienable” possession are expressed. The analysis focuses on the specific feature of the worldview of the nomads of the Great Steppe, which is linked to the means of expressing possession that emerged through grammatical reflection and has been preserved in all modern Turkic languages. On the empirical material of runic written monuments, the nature of origin and functioning of affixes of belonging in the Old Turkic language is revealed

Research on Aspect-Level Sentiment Analysis Based on Adversarial Training and Dependency Parsing

Aspect-level sentiment analysis is used to predict the sentiment polarity of a specific aspect in a sentence. However, most current research cannot fully utilize semantic information, and the models lack robustness. Therefore, this article proposes a model for aspect-level sentiment analysis based on a combination of adversarial training and dependency syntax analysis. First, BERT is used to transform word vectors and construct adjacency matrices with dependency syntactic relationships to better extract semantic dependency relationships and features between sentence components. A multi-head attention mechanism is used to fuse the features of the two parts, simultaneously perform adversarial training on the BERT embedding layer to enhance model robustness, and, finally, to predict emotional polarity. The model was tested on the SemEval 2014 Task 4 dataset. The experimental results showed that, compared with the baseline model, the model achieved significant performance improvement after incorporating adversarial training and dependency syntax relationships.

On Syntactic Complexity in English Language

English is a language of hypotaxis, which means the language depends on syntactic and lexical devices to form one sentence or sentences. The paper discusses the syntactic relations and illustrates the syntactic complexity with examples. The paper focuses on the ambiguities caused by different factors to highlight the syntactic complexity of English.

CogCol: Code Graph-Based Contrastive Learning Model for Code Summarization

Summarizing source code by natural language aims to help developers better understand existing code, making software development more efficient. Since source code is highly structured, recent research uses code structure information like Abstract Semantic Tree (AST) to enhance the structure understanding rather than a normal translation task. However, AST can only represent the syntactic relationship of code snippets, which can not reflect high-level relationships like control and data dependency in the program dependency graph. Moreover, researchers treat the AST as the unique structure information of one code snippet corresponding to one summarization. It will be easily affected by simple perturbations as it lacks the understanding of code with similar structure. To handle the above problems, we build CogCol, a Code graph-based Contrastive learning model. CogCol is a Transformer-based model that converts code graphs into unique sequences to enhance the model’s structure learning. In detail, CogCol uses supervised contrastive learning by building several kinds of code graphs as positive samples to enhance the structural representation of code snippets and generalizability. Moreover, experiments on the widely used open-source dataset show that CogCol can significantly improve the state-of-the-art code summarization models under Meteor, BLEU, and ROUGE.

Performance evaluation of Word2vec accelerators exploiting spatial and temporal parallelism on DDR/HBM-based FPGAs

Word embedding is a technique for representing words as vectors in a way that captures their semantic and syntactic relationships. The processing time of one of the most popular word embedding technique Word2vec is very large due to the huge data size. We evaluate the performance of a power-efficient FPGA-based accelerator designed using OpenCL. We achieved up to 18.7 times speed-up compared to single-core CPU implementation with the same accuracy. The proposed accelerator consumes less than 83 W of power and it is the most power-efficient one compared to many top-end CPU and GPU-based accelerators.

Voices of rape: Cognitive networks link passive voice usage to psychological distress in online narratives

Past studies of sexual assault have found that passive voice descriptions of rape elicit an increased perception of victim responsibility compared to active voice narratives (Bohner, 2001), contributing to victim blaming and the perpetuation of rape myths. Building on this, we investigate the relationship between passive/active voice usage and perception, but from the perspective of rape survivors as disclosed in their online rape narratives. We collect rape narratives from Reddit's r/sexualassault board and group them into a passive voice group and an active voice group. We detect differences between the two groups of text using a cognitive network science approach that creates network representations from text such that nodes represent words/concepts while links represent syntactic and semantic relationships between them. We systematically identify nodes that are significantly more central to one network compared to the other, thus identifying characteristic concepts that semantically differentiate the two groups of narratives. We then investigate the contexts of these concepts applying semantic frame analysis. We find that concepts related to psychological distress (e.g. PTSD, flashback) are significantly more central to passive voice narratives, providing quantitative evidence of a link between passive voice usage and an increased focus on psychological distress. We also find that family members (e.g. parent, brother) are more central to active voice narratives, suggesting a connection between active voice usage and an increased focus on others' roles in rape survivors' experiences. Our quantitative results reveal an important link between language and mental health that has valuable implications for therapeutic interventions.

A dual contrastive learning-based graph convolutional network with syntax label enhancement for aspect-based sentiment classification

Introduction: Aspect-based sentiment classification is a fine-grained sentiment classification task. State-of-the-art approaches in this field leverage graph neural networks to integrate sentence syntax dependency. However, current methods fail to exploit the data augmentation in encoding and ignore the syntactic relation in sentiment delivery.Methods: In this work, we propose a novel graph neural network-based architecture with dual contrastive learning and syntax label enhancement. Specifically, a contrastive learning-based contextual encoder is designed, integrating sentiment information for semantics learning. Moreover, a weighted label-enhanced syntactic graph neural network is established to use both the syntactic relation and syntax dependency, which optimizes the syntactic weight between words. A syntactic triplet between words is generated. A syntax label-based contrastive learning scheme is developed to map the triplets into a unified feature space for syntactic information learning.Results: Experiments on five publicly available datasets show that our model substantially outperforms the baseline methods.Discussion: As such, the proposed method shows its effectiveness in aspect-based sentiment classification tasks.

한국어 직접 증거성의 1인칭 효과에 관한 단상

This article probes evidential characteristic of -te- and -ne, working on the constraint release of 1st person agent. Root sentences with direct evidentiality show the duplicity that the use of 1st person agent is sometimes restricted but eased other times. 1st person agent is objectified as if it were the 3rd person and escapes the restriction within the framework of evidentiality. There is not enough discussion on the evidential characteristics of -te- and -ne which affect this process. In this article we propose that -te-, -ne still invoke the direct eviden- tiality based on the fact that because of the non­eye­witness evidentaility the evidential strength of -te-, -ne do not undermine the assertiveness of evidential strength of proposition realized with direct evidentiality. This evidential effect works on 1st person agent constraint easing where, speaker objectifies 1st person agent who is protagonist of the event by reducing evidential effect of participatory evidentiality. The result of this note is expected to provide fundamental description to represent syntactic relation between -te-, -ne and 1st person agent.

VulMPFF: A Vulnerability Detection Method for Fusing Code Features in Multiple Perspectives

Source code vulnerabilities are one of the significant threats to software security. Existing deep learning-based detection methods have proven their effectiveness. However, most of them extract code information on a single intermediate representation of code (IRC), which often fails to extract multiple information hidden in the code fully, significantly limiting their performance. To address this problem, we propose VulMPFF, a vulnerability detection method that fuses code features under multiple perspectives. It extracts IRC from three perspectives: code sequence, lexical and syntactic relations, and graph structure to capture the vulnerability information in the code, which effectively realizes the complementary information of multiple IRCs and improves vulnerability detection performance. Specifically, VulMPFF extracts serialized abstract syntax tree as IRC from code sequence, lexical and syntactic relation perspective, and code property graph as IRC from graph structure perspective, and uses Bi-LSTM model with attention mechanism and graph neural network with attention mechanism to learn the code features from multiple perspectives and fuse them to detect the vulnerabilities in the code, respectively. We design a dual-attention mechanism to highlight critical code information for vulnerability triggering and better accomplish the vulnerability detection task. We evaluate our approach on three datasets. Experiments show that VulMPFF outperforms existing state-of-the-art vulnerability detection methods (i.e., Rats, FlawFinder, VulDeePecker, SySeVR, Devign, and Reveal) in Acc and F1 score, with improvements ranging from 14.71% to 145.78% and 152.08% to 344.77%, respectively. Meanwhile, experiments in the open-source project demonstrate that VulMPFF has the potential to detect vulnerabilities in real-world environments.

Just pair‐merge

AbstractTwo structure‐building operations are currently posited in minimalist theory: an operation forming sets (set merge), and an operation forming ordered pairs (pair‐merge). I argue that pair‐merge is sufficient to generate syntactic relations, so set merge, also called simple merge, should be eliminated from syntactic theory on grounds of simplicity. This conclusion requires reevaluating the relationship between structure‐building and labeling of constituents for the expression of syntactic relations, because labeling plays a crucial role in this comparison of theories according to the simplicity metric. An existing labeling hypothesis, specifically Chomsky's Labeling Algorithm, is shown not to have any advantage claimed for it by comparison with the just pair‐merge hypothesis proposed here. An advantage of the only pair‐merge hypothesis is that it provides a more principled origin for the inherent asymmetry in the c‐command relation that does not follow from a theory that includes set merge.

Considerations Regarding the Paradigmatic and Syntagmatic Dimension of Occasionalisms

Vocabulary changes are an inherent part of the process of existence and development of a language system. In order to give a name to the phenomena of the surrounding reality, the human, as a creative being, not only makes use of the existing possibilities of the language system, but also resorts to possible means of creating words that are not yet used, or not used at all, and is sometimes put in the position of breaking the established lexical rules. Nonce words, being definite attributes of the speech, display a paradigmatic-syntagmatic combinatory capacity similar to that of common words, thus filling the gaps in the language system. Being, on the one hand, a means of naming through the process of its selection from the multitude of possible lexical variants, the nonce word falls within the realm of paradigmatics, and, on the other hand, being part of a context and interacting with the linguistic signs in its proximity, the nonce word also enters into relations of a syntagmatic order. The present paper proposes a paradigmatic and syntagmatic investigation, thus highlighting the various paradigmatic (hyponymy/hyperonymy, polysemy, homonymy, paronymy, synonymy and antonymy) and syntagmatic (logical-lexical, associative and syntactic relations) relationships in which nonce words can be embedded.

QUANTIFYING NUMBER IN THE NOUN PHRASE: A COMPARATIVE STUDY BETWEEN ENGLISH AND MACEDONIAN

<p>This paper discusses some of the theoretical insights for English gained by means of a contrastive analysis with Macedonian on the issue of grammatical/semantic number in the NP, with a particular focus on the features of partitivity, collectivity and distributivity reflected through marked external (grammatical) means. Quantifying the NP provides information on the fragmentation of the whole, togetherness of the whole (with non-determinedness of entities), or the individuality of each element of the whole. The quantitative characteristics are visible in the NP structure, while the syntactic relationships formally realize the connection between the quantity in the NP and semanticity. Through excerpted examples from authentic source materials (in English and Macedonian) and translations, it will be concluded that both languages have more commonalities than differences in the morphology and semantics of these features, although precision of language means is necessary to avoid miscommunication. </p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/lit/0758/a.php" alt="Hit counter" /></p>

Spatial Locative Relativization in Three African Varieties of Portuguese: Unity in Diversity and Diversity in Unity

This paper investigates the formation of spatial locative relativization in three African varieties of Portuguese. While research on relative constructions in Portuguese has deserved considerable attention in the literature, it tends to focus on the European and Brazilian varieties, with locative relativization being only marginally addressed. Using data extracted from spoken corpora of contemporary, urban varieties of Angolan, Mozambican, and Santomean Portuguese, we aim to discuss whether there is a correlation between syntactic and semantic variables and the selection of the two main locative relative morphemes, onde ‘where’ and que ‘that’. Overall, the three varieties at stake behave similarly with respect to the analyzed syntactic variables and follow the tendency found in Portuguese varieties toward the use of pied-piping and P-chopping as the dominant relativization mechanisms, independent of the syntactic relation between the antecedent and the relative clause. Semantically, we identified some fine-grained differences between the three varieties, with Santomean Portuguese generally being the outlier or one of the outliers. Crucially, definiteness of the head noun stands out as the one variable that plays a major role in the selection of the relative morpheme: [−definite] head nouns show a proportionally higher preference for que in both AP and STP, which is particularly visible with bare nouns in the latter. This motivates the hypothesis that less specified head nouns show a preference for the un(der)specified relative morpheme que. We further show that the role of language contact is at best very limited.

Topic Particles, Agreement, and Movement in an Arabic Dialect

The dialect of North Hail, a variety of Najdi Arabic, has a set of sentence-initial particles marking topics of various kinds that correspond to those argued by Frascarelli and Hinterhölzl (2007) to be characteristic of Italian and German: Shift-Topic, Contrastive Topic, and Familiar Topic. In their work, as in much other work in the cartographic tradition, a hierarchy of abstract topic heads is postulated in the C-domain, which host the topical phrases as specifiers. In North Hail Arabic, the topic heads are overt, realized as particles. Some of the topic heads mark topics by attraction to the C-domain; other particles mark topics by ϕ-feature agreement. The particles in the C-domain agree in ϕ-features with a TP-internal DP, subject or object. Analyzed in terms of Agree (Chomsky 2001, 2008), arguments and adverbials are assigned particular topic values either by agreement or by movement. The particles thus provide evidence that topicality can be a syntactic feature, inherent in lexical items (the particles) and assigned to constituents by operations familiar from standard syntactic relations like subject agreement and case. The theory articulated observes the Inclusiveness Condition, known to be a problem for the cartographic theory of topic and focus.

Assessing the Arabic Parsing Capabilities of ChatGPT and Cloude: An Expert-Based Comparative Study

Arabic, known for its rich morphological and syntactic complexity, poses significant challenges for natural language processing technologies. This study presents a comparative evaluation of two artificial intelligence tools, ChatGPT and Cloude, in their abilities to parse Arabic sentences accurately. Five sentences embodying diverse linguistic features were selected, and the parsing outputs were evaluated by three Arabic language experts. The results revealed a clear performance gap, with Cloude outperforming ChatGPT in overall accuracy (72.9% vs. 33.3%). Cloude excelled in handling morphological complexities and basic syntactic relationships but encountered difficulties with highly idiomatic expressions and ambiguous constructions. Conversely, ChatGPT struggled with complex morphology and long-distance dependencies. The findings highlight the importance of developing specialized Arabic language processing tools while acknowledging the potential of general-purpose language models with further fine-tuning. Recommendations include incorporating domain-specific knowledge, leveraging transfer learning, and exploring ensemble approaches to enhance the accuracy and robustness of AI-based Arabic parsing systems.

Graph Convolutional Network with Syntactic Dependency for Aspect-Based Sentiment Analysis

AbstractAspect-based sentiment analysis (ABSA) aims to mine the sentiment tendencies expressed by specific aspect terms. The studies of ABSA mainly focus on the attention-based approaches and the graph neural network approaches based on dependency trees. However, the attention-based methods usually face difficulties in capturing long-distance syntactic dependencies. Additionally, existing approaches using graph neural networks have not made sufficient exploit the syntactic dependencies among aspects and opinions. In this paper, we propose a novel Syntactic Dependency Graph Convolutional Network (SD-GCN) model for ABSA. We employ the Biaffine Attention to model the sentence syntactic dependencies and build syntactic dependency graphs from aspects and emotional words. This allows our SD-GCN to learn both the semantic relationships of aspects and the overall semantic meaning. According to these graphs, the long-distance syntactic dependency relationships are captured by GCNs, which facilitates SD-GCN to capture the syntactic dependencies between aspects and viewpoints more comprehensively, and consequently yields enhanced aspect features. We conduct extensive experiments on four aspect-level sentiment datasets. The experimental results show that our SD-GCN outperforms other methodologies. Moreover, ablation experiments and visualization of attention further substantiate the effectiveness of SD-GCN.

Comparative Analysis of Word Embeddings for Multiclass Cyberbullying Detection

Cyberbullying has emerged as a pervasive concern in modern society, particularly within social media platforms. This phenomenon encompasses employing digital communication to instill fear, threaten, harass, or harm individuals. Given the prevalence of social media in our lives, there is an escalating need for effective methods to detect and combat cyberbullying. This paper aims to explore the utilization of word embeddings and to discern the comparative effectiveness of trainable word embeddings, pre-trained word embeddings, and fine-tuned language models in multiclass cyberbullying detection. Distinguishing from previous binary classification methods, our research delves into nuanced multiclass detection. The exploration of word embeddings holds significant promise due to its ability to transform words into dense numerical vectors within a high-dimensional space. This transformation captures intricate semantic and syntactic relationships inherent in language, enabling machine learning (ML) algorithms to discern patterns that might signify cyberbullying. In contrast to previous research, this work delves beyond primary binary classification and centers on the nuanced realm of multiclass cyberbullying detection. The research employs diverse techniques, including convolutional neural networks and bidirectional long short-term memory, alongside well-known pre-trained models such as word2vec and bidirectional encoder representations from transformers (BERT). Moreover, traditional ML algorithms such as K-nearest neighbors, Random Forest, and Naïve Bayes are integrated to evaluate their performance vis-à-vis deep learning models. The findings underscore the promise of a fine-tuned BERT model on our dataset, yielding the most promising results in multiclass cyberbullying detection, and achieving the best-recorded accuracy of 85% on the dataset.

Cross-linguistic Dependency Length Minimization in scientific language

Abstract We use Universal Dependencies (UD) for the study of cross-linguistic diachronic syntactic complexity reduction. Specifically, we look at whether and how scientific English and German minimize the length of syntactic dependency relations in the Late Modern period (ca. 1650–1900). Our linguistic analysis follows the assumption that over time, scientific discourse cross-linguistically develops towards an increasingly efficient syntactic code by minimizing Dependency Length (DL) as a factor of syntactic complexity. For each language, we analyse a large UD-annotated scientific and general language corpus for comparison. While on a macro level, our analysis suggests that there is an overall diachronic cross-linguistic and cross-register reduction in Average Dependency Length (ADL), on the micro level we find that only scientific language shows a sentence length independent reduction of ADL, while general language shows an overall decrease of ADL due to sentence length reduction. We further analyse the syntactic constructions responsible for this reduction in both languages, showing that both scientific English and German increasingly make use of short, intra-phrasal dependency relations while long dependency relations such as clausal embeddings become rather disfavoured over time.

Differentiation of the Subject and the Complement in French Sentences: Difficulties of Translation

This article deals with the problem of the non-prepositional members of the French narrative sentence. The main goal is to clarify the basic principles of the positioning of the subject and the complement, their interchangeability, the principles of localization in written speech. Within the framework of the work, descriptive, comparativehistorical, comparative and semantic-stylistic methods of language research were used. The results of the analysis of grammatical reference books and fiction of the XVIII-XX centuries made it possible to prove the connection of the position of the subject and the complement with their syntactic function. The semantic component in the expression of syntactic relations is analyzed. Advanced hypothesis were verified with the examples of four translations of the classic Russian novel of the XIX century. The research will expand the comprehension of subject and complement in the French narrative sentence and, perhaps, encourage linguists to further study this phenomenon not only in analytical, but also in inflectional languages.

Марийские словосочетания с опорными отсубстантивными именами прилагательными

the Mari language, adjectival phrases, including denominative adjectives, function quite actively in expressing the features of a subject, concretized from various sides. However, phrases with such adjectives, their lexicalgrammatical, syntactic and semantic features have not yet been subjected to special analysis. Objective: to analyze typical models of Mari phrases with adjectives formed using productive and unproductive suffixes from nouns. Research materials: the study was carried out on the basis of the factual material in the Meadow-Eastern Mari literary language, presented in the part of the electronic corpus of the Mari language (the volume is about 20 million word uses). 88 Вестник угроведения. Т. 14. № 1 (56). 2024. Results and novelty of the research: based on the results of systematization of the identified phrases with denominative adjectives and their structural analysis, models (schemes) of the studied syntactic constructions (five in total) were established. The main morphological properties of the components are determined – the expression of a reference word by adjectives with the suffixes -an (-yan), -se (-so, -sӧ), -dyme (-dymo, -dymӧ), -lyk, -ash (-yash) and dependent adjectives, nouns, numerals, pronouns and participles of different categories. They indicate that denominative adjectives as a whole retain the combinable properties of nouns that served as a word-formation basis for them. The novelty of the study lies in the fact that for the first time it presents structural models of Mari attributive phrases with reference denominative adjectives: ADJ + ADJclass.adn., N + ADJclass.adn., NUM + ADJclass.adn., PRON + ADJclass.adn., PTCP + ADJclass. adn. The syntactic relation (determinative) and grammatical connection (adjunction) in which the components of phrases are located. The results of the work can be used in comparative study of similar syntactic units in other Finno-Ugric, as well as in contacting unrelated languages, in study of the features of syntax of Mari phrases in universities and schools