Linguistic Model Research Articles

Лингвотопологическое моделирование процесса перевода: к общим основаниям

Cognitive research in translation is currently at its peak. The authors studied L. V. Kushnina’s synergetic concept of translation space in an interaction with the topological fields of translation to develop a linguistic topological model of translation as a process. The analysis yielded graphic constructs represented by topologically structured spaces that illustrate the process of interlingual and intercultural interaction. The research relied on P. A. Florensky’s idea of the language of spatial objects and the provisions of philological and translation topology developed by L. V. Polubichenko, who studied the variant vs. invariant patterns of different philological objects, from words to texts. The current Chinese translation studies show similar ideas (see: Wu Dan, Ding Xiuhei, Zhao Genglin, Jiang Shengnan, etc.). The article visualizes the modern translation paradigm from the perspective of linguistic topology. Translation is a multifunctional verbal and mental activity that unites various objects in the spaces between the sender, the translator, and the recipient. The resulting topological translation model represents the linguistic topology of interlingual communication at the intersection of cognitive topology, linguistics, translation theory and practice, and cognitive translation studies.

IMPROVING THE EFFICIENCY OF ENERGY COMPANIES BY OPTIMISING THE DISTRIBUTION OF COSTS FOR MAINTENANCE AND REPAIR OF ELECTRICAL EQUIPMENT

At present, there is a high emergencies probability in the electric power system as an electrical equipment failures result caused primarily by the objectively existing ageing and its service life significant exhaustion. Emergencies arising from electrical equipment failures lead to the dynamic stability violation of power systems and power system load nodes by voltage, cascading accidents and, as a result, technological processes disruption of consumer enterprises, which is accompanied by significant losses. One of the ways to prevent sudden failures or minimise the risk of their occurrence is to ensure rational and efficient operation management and reduce the operating equipment cost without risking a decrease in the power system reliability. To solve the optimal distribution problems of power company operating costs, taking into account the objectively existing limiting factors, a generalised balance sheet model of cost allocation by electrical equipment groups and units is proposed, which is a total costs function of an operating factors number. Mathematical models for calculating the electrical equipment share participation coefficients in the costs formation for its repair and maintenance for each of the identified factors have been obtained. A linguistic mathematical model for determining generalised estimates of the electrical equipment units and groups significance degree in the repair and maintenance costs distribution formation, taking into account the main operational factors, has been proposed. An algorithm and software for modelling the modes of the electric power system with an electrical connections scheme of electric networks with a 35-330 kV voltage have been developed to assess the operation risk index and power company repair and maintenance costs distribution. The estimated power company repair and maintenance costs distribution according to the current technical equipment characteristics and power system subsystem modes was performed.

O Problema da Representação na Metafísica Analítica: Analisando a Falácia Representacional no debate sobre o Tempo

This paper critiques the contemporary reliance on linguistic analysis in metaphysics, arguing that it leads to the representational fallacy – a tendency to derive ontological conclusions from linguistic properties rather than from reality. We trace the historical roots of this issue to the Kantian and linguistic turns, which shifted metaphysical focus from ontology to semantics, situating language at the center of the philosophical inquiry. We examine how this fallacy manifests in the debate between A-theorists and B-theorists of time. Both camps rely on Structural Linguistic Representationalism – the assumption that language directly mirrors reality –, presupposing a necessary correspondence between linguistic structures and ontological facts. We argue that this dependence is misguided, as language alone cannot adequately capture the fundamental nature of reality. In addressing this issue, we analyze two approaches within the B-theory of time: the truth-conditions approach and the truthmaker approach. We contend that the truth-conditions approach reiterates the representational fallacy by deriving ontological conclusions from linguistic structures. In contrast, the truthmaker approach presents a more promising framework by shifting focus from language to ontology, grounding metaphysical explanations in the structure of reality in itself. We conclude that the pursuit of an absolute linguistic model to reveal the ultimate nature of reality is unjustified and hinders metaphysical investigation. Instead, we advocate grounding ontological inquiries in reality, using language as a tool rather than a source of metaphysical truth.

Sustainable pavement maintenance and rehabilitation planning using the quantum cognitive trust network

Selecting appropriate pavement maintenance strategies can enhance resource efficiency, reduce environmental pollution, prolong road lifespan, and improve societal sustainability. Currently, most models, which selecting pavement maintenance options, exhibit high subjectivity and low reliability. This study employs an innovative probabilistic linguistic multi-attribute decision-making model basing on the quantum cognitive theory. Therefore, the model focuses on expert decision-making groups for pavement maintenance strategies. It considers the trust networks within the decision-making process, as well as the sequential and interference effects arising from experts' belief biases and behavioral interactions. By doing so, it facilitates a more scientific selection of optimal strategies among multiple maintenance options. The proposed method has been validated through practical case studies of road segments, and its scientific validity and effectiveness have been further corroborated through sensitivity analysis.

Malay phoneme-based subword news headline generator for low-resource language

The booming of technology has significantly increased the amount of news articles for readers. The headline of news plays an essential role in attracting readers. Traditionally, crafting the news headline is a manual task at the news desk. The motivation of this paper is to address the issues faced in low resource languages, such as the Malay language. The main contribution of this paper is a new hybrid model based on extractive- and abstractive-based text summarization with the integration of a geographical linguistics model; a Malay phoneme-based subword embedding has been developed to solve the complex morphological issue in the Malay language-based computational linguistic applications. The experiment involves various sequence-to sequence (seq2seq) models to generate the Malay news headlines. Besides that, the out-of-vocabulary (OOV) is assessed in the models. From the experiment, the proposed hybrid text summarization model shows significant improvement over the baseline models above 11.00 in ROUGE-1, 4.00 ROUGE-2, and 11.00 in ROUGE-L. The proposed model can reduce the OOV rate to below 15%.

Differences in the Parsing of the Present Tense Verb in Qur’anic Verses in Order to Develop Arabic Language Skill

This research aims to expose the meanings, and the difference in the structure of the present tense in the frequent Qur’anic readings with an explanation of the impact of this difference in the noble verse, and it aims to provide correct linguistic models that contribute to building the linguistic ability of the students. The methodology was followed in this research, and this research included an introduction, a methodology section, result and discussion, and the last is conclusion. As for the introduction, it defined the subject of the research, the reasons for its selection, its objectives, the method used, the basic rule, previous studies, and the study plan. It focuses on linguistic analogy, meaning of harf jarr, linguistic skills, its definition and methods of obtaining. The present tense verb is preceded by harf lām and atf that considering the lām as a reasoning. It is not a condition for the present tense to be in the accusative case that the lam be clear in its reasoning. The lam and atf has function in the present tense verb “to be made” was read with the kasra of the lam and the accusative, and with the sukun of the lam and the jazm in the context of al-fi’l al-mudhari’, and it is clear that the reading of the kasra of the lam and the accusative was directed by considering the lam for the reasoning and the accusative of “to be made” with (that) is implicitly permissible.

НОМІНАТИВНЕ ПОЛЕ СУБКОНЦЕПТУ ΜΑΝΙΑ В ДАВНЬОГРЕЦЬКІЙ МОВІ

The article focuses on the analysis of the nominative fi eld of the sub-concept ΜΑΝΙΑ in the Ancient Greek language, which is one of the components of the macro-concept DISEASE, covering the entire complex of signs which characterize abnormal human behavior. For the completeness of the analysis, a number of methods were used, in particular, the descriptive one, the method of conceptual, etymological and component analyses, and linguistic modeling. As a result of the study, it was estab lished that the mentioned nominative fi eld is monocentric with a clearly defi ned core and paranuclear and peripheral zones. The core consists of the lexemes formed from the root μαν- (μανία, μανικóς, μαίνομαι together with composi tes), which are the most frequent ones and most fully realize the “lack of mental adeq uacy” sema. The lexeme μανια is a term commonly used both in medical texts and in the texts of other genres. In various contexts, it denotes extreme and excessive behavior and mental disorders. The paranuclear zone includes lexemes which characterize various deviations from the norm and differ from the keyword-name of the concept in shades of meaning. As a result of the semanti c analysis, the lexemes were divided into four microfi elds: 1) “inadeq uacy of thinking and actions” (ἀβελτερία, ἀλογιστία, ἀεσιφροσύνη, ἀλλοφρονέω, etc.); 2) “state of aff ect” (ἀδημονέω, δαιμονάω, λύσσα, μαργότης, ἔκστασις, ἔκστασις , etc.); 3) “exaltation” (βακχεύω and verbs derived from it, βάκχευσις, ἐνθουσιάζω, ἐνθουσίασις, etc.); 4) “morbid mental state” (παρανοέω, παράνοια, παραφροσύνη, φρενιτίζω, etc.). The paranuclear zone also includes the commonly used lexemes to denote disease (νόσος, ἀρρωστία, ἀσθένεια) specifi ed by the nouns φρήν, νοῦς, ψυχή. In the peripheral zone, the near and far periphery is distinguished. The fi rst includes words in which the “lack of mental adequacy” seme is secondary although motivated by the main lexical meaning (e.g., ἀάω, ἀνόητος, ἄλογος). The far periphery is represented by the lexemes which acquire close (or identical) to the semantics of the nuclear lexeme meanings only in a certain context; however, these meanings are not motivated by their basic lexical semantics, arising as a result of metaphorization or ellipsis (eg: ἄλη, ἐλλέβορος, πῦρ, λῆρος, παρακοπή, etc.). Key words: Ancient Greek language, nominative fi eld, sub-concept, lexeme, core, periphery.

ЧУЖИНКА: ПРОЄКЦІЯ АЛІЄНОВАНОЇ ЖІНОЧОЇ ОСОБИСТОСТІ В МАЛІЙ ПРОЗІ ДЖИН РІС

The present study examines the prose texts written by Jean Rhys (1890–1979), a British woman writer of modernist literature. It is based on the perusal of several short stories, among others: “In the Rue de l’Arrivée”, “A Night”, “La Grosse Fifi ”; “Outside the Machine”, “The Lotus”, “Till September, Petronella”, and “Let Them Call It Jazz” which were included in two collections of her fi ction – “The Left Bank and Other Stories” (1927) and “Tigers are Better-Looking” (1968). The purpose of the paper is to analyze the author’s concept of lonely, alienated, vulnerable and defenseless female individuals which forms the core of her artistic imagination. The scientifi c research uses gynocritics as its methodological background. According to the theory of its critic E. Showalter, it was possible to identify four models of woman writing having been realized in Jean Rhys’s short prose: a) psychoanalytic; b) biological; c) linguistic and d) cultural. The psychoanalytic model of Rhys’s fi ction is viewed in the context of both the emotional and conscious worlds of an alienated female identity. The woman protagonist being rejected by a hostile society, becomes a stranger and outcast. The feelings of deep loneliness, pain, fear, and loss, intertwined with the death wish and with resistance, determine the constants of female existence. An important aspect of woman personality is the dimension of female friendship and solidarity. The biological model of short prose by J. Rhys is explored through the concept of female corporeality. The corporeal vulnerability of women protagonists is shown as a form of the fragility of their inner world. An unattractive woman image created by the author manifests the psychological uncertainty and brittleness of female characters. And a metaphor of the looking glass as a refl ection of their appearance reveals the concept of the female Self as a loss which varies from the loss of a woman’s youth to the loss of feminine identity or its splitting. The linguistic model of Rhys’s short stories testifi es to the bipolar artistic world of the writer, which is demonstrated at the confrontation between French and British cultures with the signs of laconism, subtext, and silence as the main features of her writing manner. Meanwhile, the cultural model of J. Rhys’s prose is read through the prism of the intertextual discourse of literature, music and painting. Key words: loneliness, fear, suff ering, alienation, hostility, corporeality, vulnerability.

Hesitant fuzzy linguistic preference consistency-driven consensus model with large-scale group interaction measure for venture capital investment selection

Recently, consensus-based large-scale group decision making (LSGDM) has been widely interactive with the study of social network, clustering and trust-based concepts. This study develops a novel hesitant fuzzy linguistic preference consistency-driven consensus model with interaction measure for large-scale group decision makers (DMs) in social networks. Firstly, directed social network is constructed by measuring the similarity between incomplete hesitant fuzzy linguistic preference relation (HFLPR) matrices. Community detection method is further conducted to categorize DMs into several communities. Secondly, driven by exploring the consistency of HFLPR matrices and interactive trusts between DMs, a novel optimization model is established to estimate the missing elements. Thirdly, the 2-order additive fuzzy measures of different coalitions between divided communities for capturing their fully or partially interactions are derived by a consistency-based optimization model. Accordingly, the attitudinal Choquet integral operator is employed to aggregate preferences into the collective one. Fourthly, a consensus improving mechanism is devised to achieve the unanimous agreement of DMs characterized by the bounded confidence. Personalized and specific adjustment scales obtained by investigating interval consistency of HFLPRs are provided in support of DMs’ modifications. Finally, an illustrative case on syndicated venture capital investment selection is conducted and related simulation analyses are performed to elucidate the feasibility and validity of the proposed methods. The comparisons with other approaches reveal the superiority and improvement of our proposal.

Multisensory Integration in Lexical Processing: Predicting Word Recognition Through Cross-Modal Capability

The integration of multisensory in lexical processing has garnered significant interest in cognitive linguistics and psycholinguistics. This study investigates the predictive power of cross-modal capability (CMC) on word recognition, focusing on how sensory integration influences cognitive processing and lexical decision-making. By collecting five-sense ratings for synesthetic adjectives, we computed the cross-modal capability for each word. We then conducted regression analyses to examine the relationship between these indices and reaction time in lexical decision tasks. Our findings reveal that words with higher cross-modal capabilities, as measured by CMC, are recognized and processed more quickly. This superior predictive power of CMC underscores the importance of sensory associations in lexical processing and highlights the need for comprehensive indices that account for multiple sensory inputs. These results provide deeper insights into the cognitive mechanisms underlying word recognition and processing speed, suggesting that CMC is a better independent variable for partially predict lexical cognition and recognition. This research contributes to our understanding of multisensory integration in lexical processing and has implications for models of word recognition and cognitive linguistics.

Онтологическое моделирование как средство семантизации терминологической лексики в процессе обучения иностранному языку специальности

The study provides a rationale for the use of ontological modeling techniques for teaching the language of a specialty to foreign students studying educational programs in Russian. Ontologies of subject areas, defined as conceptualization specifications, are widely used in computational linguistics to create formalized knowledge management systems. In the context of internationalization, globalization and a colossal increase in the volume of industry knowledge, linguaprofessional skills of interlingual modeling and systematization of conceptual areas of various professions should become a basic element of foreign-language professional communicative competence. This study presents the experience of applying linguistic ontological modeling techniques as a means of improving the effectiveness of terminology teaching, in particular, in terms of semantization of terminological units that form the conceptual sphere of professional communication of students in non-linguistic areas of training. In the course of this study, positive experience was accumulated in applying linguistic ontological modeling techniques, proving the feasibility of using ontologies as a means of semantization of industry terminology. In the applied aspect, the approach we propose can be used for ontological modeling of foreign language courses for special purposes, as well as for creating digital didactic products with an extended list of functions that form a linguistic and professional virtual learning environment.

Avances y retos del plurilingüismo en los poderes públicos del estado españolProgress and challenges of multilingualism in the public powers of the Spanish state

Abstract More than four decades after the promulgation of the Spanish Constitution of 1978, its provisions on linguistic matters have been defined, through a dynamic and constructive process, by the state and regional legislators, and by the jurisprudence of the Constitutional Court, to the point of allowing a significant advance of minority languages that had been neglected for decades. Over time, this dynamic, not exempt from controversy, as corresponds to an open and plural society, has revealed some imbalances due to the asymmetry of the constitutional linguistic model, for example the scarce presence and promotion of linguistic diversity at the state level.

Quantum mechanics and statistical physics: Novel frameworks for enhancing natural language processing

Abstract. This article explores the pioneering application of principles from quantum mechanics and statistical mechanics to the field of natural language processing (NLP). By drawing analogies between physical phenomena such as quantum entanglement, phase transitions, and statistical ensembles, and linguistic concepts like semantic relationships, language use dynamics, and lexical diversity, we offer a novel perspective on language analysis and processing. Quantum linguistic models, leveraging the intricacies of entanglement and quantum probability, provide a framework for understanding complex semantic networks and enhancing computational efficiency through quantum computing. Meanwhile, statistical mechanics inspires models for capturing lexical diversity and understanding the evolution of language patterns, akin to phase transitions in physical systems. This interdisciplinary approach not only deepens our understanding of linguistic phenomena but also introduces advanced mathematical and computational techniques to improve NLP tasks.

Электронный корпус татарского языка на базе модели лингвистических графов знаний

The article presents a new version of the electronic corpus of the Tatar language, updated based on a linguistic knowledge graph model for Turkic languages. This new version of the corpus allows for information description across multiple linguistic levels: morphonological, syntactic, and semantic, through the use of knowledge graphs to represent linguistic data. This approach enhances corpus functionality, enabling searches that incorporate syntactic and semantic information. A distinctive feature of the electronic corpus implementation is that the model employed aligns closely with the structural and functional characteristics of Turkic languages and serves as a foundation for developing various software products for semantic text processing in Turkic languages. In particular, these products include the linguistic portal "Turkic Morphme" and the new version of the Tatar language electronic corpus, "Tugan Tel.".

On Ronald Langacker’s Semanticocentrism

Ray Jackendoff understands that Noam Chomsky’s linguistic models distort the nature of language due to its ‘syntactocentrism.’ Coined by Jackendoff, this term means Chomsky’s assumption that the syntactic component of language is central while the phonological and semantic components are merely interpretive. Jackendoff also states that since the 1970s many researchers have made a mistake opposite to Chomsky’s, by denying that syntax has a relevant role in grammar. Considering such a scenario, this paper analyzes Ronald Langacker’s cognitive grammar, in which syntax is replaced by semantics as the central linguistic component. The goal is to show how Langacker misrepresented language structure by doing so.

Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making

In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article introduces a scenario network-based approach for managing cybersecurity projects, utilizing fuzzy linguistic models and a Takagi–Sugeno–Kanga fuzzy neural network. Drawing upon L. Zadeh’s theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. Fuzzy production rules are employed for decision-making, while the Mamdani fuzzy inference algorithm enhances interpretability. This approach enables multi-scenario planning and adaptability across multi-stage cybersecurity projects. Preliminary results from a research prototype of an intelligent expert system—designed to analyze project stages and adaptively construct project trajectories—suggest the proposed approach is effective. In computational experiments, the use of fuzzy procedures resulted in an over 25% reduction in errors compared to traditional methods, particularly in adjusting project scenarios from pessimistic to baseline projections. While promising, this approach requires further testing across diverse cybersecurity contexts. Future studies will aim to refine scenario adaptation and optimize system response in high-risk project environments.

Linguistic models of abusive language

ABSTRACT Abusive language and hate speech that incites violence is a growing problem for governments trying to manage social media posts on the internet. Such language incites civil disorder by angering target individuals and groups, and tends to reinforce the identity and cohesiveness of those who use it. Abusive language is an intelligence target because it can be a leading indicator of violence. Black box predictors can achieve high accuracy but they reveal nothing about the structures, both social and technical, that underlie abusive language. We build a set of abusive language predictors leveraging both social constructs such as otherness, and linguistic properties. A stacking predictor then determines the most significant component predictors. We achieve prediction accuracy of 96.5%, with an 88% accuracy for abusive documents. This is an improvement of more than 16% points in detecting abusive documents compared to a popular empirically derived predictors, and provides insights into the mechanics of abusive language.

Incoherent coherence? Using systemic functional linguistics to improve oral language assessment literacy

AbstractThis paper discusses the challenges of defining coherence in the context of oral language assessment literacy and proposes that better understanding of the construct can be achieved through a systemic-functional linguistic lens. Coherence is taken to be a foundational quality of written and spoken discourse and is a standard feature in the assessment rubrics of most large-scale English-language oral proficiency tests such as IELTS, TOEFL, and Cambridge. However, the precise meaning of coherence, and how it can be identified in language, is difficult to grasp. This has implications for the makers and raters of tests as well as the candidates who take them. The literature suggests coherence is the most troublesome category to rate reliably and validly. The systemic-functional linguistic (SFL) model of language provides a rich description of coherence, but has been under-utilised by the language testing industry to date. In this paper we examine three aspects of coherence: (1) how it is currently conceptualised in oral language proficiency testing; (2) how it is conceptualised in linguistics in general and SFL in particular; and (3) how insights from SFL could inform the evaluation of coherence in oral language proficiency examinations.

Contextualising topic models in corpus linguistics: Opportunities and challenges

Contextualising topic models in corpus linguistics: Opportunities and challenges

A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets

Sentiment analysis is a pivotal tool in understanding public opinion, consumer behavior, and social trends, underpinning applications ranging from market research to political analysis. However, existing sentiment analysis models frequently encounter challenges related to linguistic diversity, model generalizability, explainability, and limited availability of labeled datasets. To address these shortcomings, we propose the Transformer and Attention-based Bidirectional LSTM for Sentiment Analysis (TRABSA) model, a novel hybrid sentiment analysis framework that integrates transformer-based architecture, attention mechanism, and recurrent neural networks like BiLSTM. The TRABSA model leverages the powerful RoBERTa-based transformer model for initial feature extraction, capturing complex linguistic nuances from a vast corpus of tweets. This is followed by an attention mechanism that highlights the most informative parts of the text, enhancing the model’s focus on critical sentiment-bearing elements. Finally, the BiLSTM networks process these refined features, capturing temporal dependencies and improving the overall sentiment classification into positive, neutral, and negative classes. Leveraging the latest RoBERTa-based transformer model trained on a vast corpus of 124M tweets, our research bridges existing gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy and relevance. Furthermore, we contribute to data diversity by augmenting existing datasets with 411,885 tweets from 32 English-speaking countries and 7,500 tweets from various US states. This study also compares six word-embedding techniques, identifying the most robust preprocessing and embedding methodologies crucial for accurate sentiment analysis and model performance. We meticulously label tweets into positive, neutral, and negative classes using three distinct lexicon-based approaches and select the best one, ensuring optimal sentiment analysis outcomes and model efficacy. Here, we demonstrate that the TRABSA model outperforms the current seven traditional machine learning models, four stacking models, and four hybrid deep learning models, yielding notable gain in accuracy (94%) and effectiveness with a macro average precision of 94%, recall of 93%, and F1-score of 94%. Our further evaluation involves two extended and four external datasets, demonstrating the model’s consistent superiority, robustness, and generalizability across diverse contexts and datasets. Finally, by conducting a thorough study with SHAP and LIME explainable visualization approaches, we offer insights into the interpretability of the TRABSA model, improving comprehension and confidence in the model’s predictions. Our study results make it easier to analyze how citizens respond to resources and events during pandemics since they are integrated into a decision-support system. Applications of this system provide essential assistance for efficient pandemic management, such as resource planning, crowd control, policy formation, vaccination tactics, and quick reaction programs.