Semantics Of Language Research Articles

Модально-компаративные конструкции с семантикой кажимости в тувинском языке

The paper examines analytical, synthetic, and lexical means of expressing modal-comparative semantics in the Tuvan language. Complex analytical constructions encompass constructions containing the first participial component, modal operators deg ‘as if,’ yshkash ‘as if,’ and the verb bol ‘to be’ in the adverbial form on -(Y)p: boop. Simple synthetic forms include the verb predicate with the affix -ZYg. Lexical means include verbs denoting pretended behavior, also characterized by the meaning of a hypothetical comparison, forming the foundation for the semantics of pretense. Constructions with modal operators deg ‘as if’, yshkash ‘as if,’ and the predicate with the affix -ZYg are used to express a comparison with an imaginary standard (an unreliable comparison). The construction with the word boop and special verbs of pretended behavior are also distinguished by a comparative meaning in their semantics. Analytical and synthetic structures containing modal operators of appearance indicate the inclusion of the perceiving subject’s worldview and personal experiences in linguistic reflection. The meaning of an unreliable comparison is the basis for the formation of the meaning of seemingness. The construction Tv=GAn + yshkash/deg conveys the semantics of seemingness without any additional nuances. Verb forms with the affix -ZYg express the semantics of seemingness based on the speaker’s emotions and imagination. The construction Tv=GAn + boop identifies the object of comparison with the standard and expresses an apparent feigned simulative action.

The new language corpus: Exploring perception of graphemes "P" and "B" in Serbian Cyrillic and Latin scripts according to semantical context

Language corpus serves as an essential instrument in facilitating research endeavors within the domain of natural language processing. As an illustration of such a language corpus, the paper presents part of the conducted research. Notably, the Serbian language demonstrates a deficiency in its linguistic resources, a concern accentuated by its unique status as one of the few languages characterized by a bialphabetical system, i.e., although in official use only the Cyrillic alphabet is used, in the Serbian language the Latin alphabet is also used in parallel. The Serbian Cyrillic alphabet in modern times is threatened by various influences of globalization, i.e., the more dominant use of the Latin alphabet in everyday life. The primary goal of the research is to examine the perceptual differences between the Latin and Cyrillic alphabets. For the purposes of the research, a sample of subjects whose native language is not Serbian was used, so that the subjects would not be compelled by previous knowledge of the semantics of the Serbian language. The subjects would have to know at least the basics of the Serbian language and the rules of reading and writing to be able to understand the questionnaire, simplified with vocabulary for level A2. Within this paper, a segment of this research will be presented through several compared variables and predominantly by examining whether the graphemes "P" and "B" can cause confusion because these graphemes in Serbian Cyrillic and Latin have different phonetic pronunciations. The research showed that the answers given by the subjects were in a higher percentage perceived in the reverse script of the questionnaire (if the questionnaire was in Cyrillic, the perception of unclear words by a higher percentage of subjects was in Latin, and vice versa).

The concepts of “Motherland” and “State” in Russian language

The subject of the study is the concepts “Motherland” and “State”, their synonyms and associative images in Russian songs and Russian language, including Russian phraseological units, proverbs and sayings. The first concept is understood as a small homeland and a large homeland. Each person has his own small homeland, which is associated with place of birth, family, house, street, village, town, etc. The large homeland in a broad sense refers to the native country. The small homeland is part of the big homeland. These concepts play a role in the formation of Russian person’s national consciousness, spiritual values, worldview, character and personality. The purpose of this work is to clarify their meanings and differences in Russian language and in Russian people’s consciousness. Research methods include descriptive, associative, comparative methods, methods of synthesis and generalization, and semantic analysis. When analyzing and comparing the concepts “Motherland” and “State”, this work examines the images reflected in the texts of Russian songs about these concepts and language examples in Russian phraseological dictionaries. The conducted research shows that the term “Motherland”, associated with national history, culture and ethnic identity, has a spiritual psychological flavor. The term “State” with a social meaning is defined as a political, legal, economic form of organization of human society. This concept is associated with the images of “law”, “authority”, “army/armed force”, “citizen”, etc. Clarifying the differences in their semantics in Russian language helps speakers of other linguistic cultures to better understand their significance in Russian people’s history, culture and consciousness, their role and influence on the formation of Russian people’s national psychology and spiritual world. The study of these concepts has prospects for expanding foreign students' vocabulary, deeper understanding of the Russian language, culture, people, its spiritual world and their relationships and enriching their cultural, linguistic and personal experience.

Stable word complexes of comparative semantics with a component-demonym in the Swedish and Russian languages

The article is devoted to identifying of general patterns and distinctive features in the structure, meaning and evaluation of stable verbal complexes of comparative semantics of the Swedish and Russian languages with a component-demonym. Stable verbal complexes of comparative semantics with a demonic component are present in both the Swedish and Russian languages. There is a significant quantitative disproportion between stable comparisons and paremias of comparative semantics of both languages, but in the Swedish language in favor of proverbs, and in Russian — in favor of stable comparisons. It can be assumed that the names of evil spirits are more taboo in Swedish stable comparisons, and also comparative phraseological units in general were less subject to lexicographic fixation. The studied proverbs of the Swedish and Russian languages are quantitatively comparable. Structurally, the similarity of the proverbs of the two languages is manifested in the presence of such models as sentences with opposition introduced by adversative conjunctions, and sentences with the presence of a component in the comparative degree. But the analyzed Swedish proverbs are characterized by such models as sentences with adjacent components, identity sentences and sentences built according to the syntactic model “better… than…”, which are absent in the studied material of the Russian language. In turn, in the analyzed Russian proverbs one can note models that are absent in Swedish ones: sentences built on the principle of syntactic parallelism and collapsed comparison.

Self-Supervised Representation Learning With Spatial-Temporal Consistency for Sign Language Recognition.

Recently, there have been efforts to improve the performance in sign language recognition by designing self-supervised learning methods. However, these methods capture limited information from sign pose data in a frame-wise learning manner, leading to sub-optimal solutions. To this end, we propose a simple yet effective self-supervised contrastive learning framework to excavate rich context via spatial-temporal consistency from two distinct perspectives and learn instance discriminative representation for sign language recognition. On one hand, since the semantics of sign language are expressed by the cooperation of fine-grained hands and coarse-grained trunks, we utilize both granularity information and encode them into latent spaces. The consistency between hand and trunk features is constrained to encourage learning consistent representation of instance samples. On the other hand, inspired by the complementary property of motion and joint modalities, we first introduce first-order motion information into sign language modeling. Additionally, we further bridge the interaction between the embedding spaces of both modalities, facilitating bidirectional knowledge transfer to enhance sign language representation. Our method is evaluated with extensive experiments on four public benchmarks, and achieves new state-of-the-art performance with a notable margin. The source code is publicly available at https://github.com/sakura/Code.

Effective Approach for Fine-Tuning Pre-Trained Models for the Extraction of Texts From Source Codes

This study introduces SR-Text, a robust approach leveraging pre-trained models like BERT and T5 for enhanced text extraction from source codes. Addressing the limitations of traditional manual summarization, our methodology focuses on fine-tuning these models to better understand and generate contextual summaries, thus overcoming challenges such as long-term dependency and dataset quality issues. We conduct a detailed analysis of programming language syntax and semantics to develop syntax-aware text retrieval techniques, significantly boosting the accuracy and relevance of the texts extracted. The paper also explores a hybrid approach that integrates statistical machine learning with rule-based methods, enhancing the robustness and adaptability of our text extraction processes across diverse coding styles and languages. Empirical results from a meticulously curated dataset demonstrate marked improvements in performance metrics: precision increased by 15%, recall by 20%, and an F1 score enhancement of 18%. These improvements underscore the effectiveness of using advanced machine learning models in software engineering tasks. This research not only paves the way for future work in multilingual code summarization but also discusses broader implications for automated software analysis tools, proposing directions for future research to further refine and expand this methodology.

A Semi-Automated Approach to Process Discovery from Multiple Natural Language Documents

The discovery of process information in natural language documents is hindered by the innate ambiguity of natural language. This issue is even more challenging when process information is spread throughout several documents, each with the possibility of different formats, writers, and original purposes, that must be read and interpreted individually by a business analyst before their information can be uncovered. This work presents a semi-automated approach to process discovery from multiple documents simultaneously, making use of semantic similarity measures and natural language processing techniques to identify, gather, and extract process information from these documents. An experiment is conducted with this approach to demonstrate its use and results.

Loanwords and polysemy: An investigation of specialized domain lexis in Middle English

This study investigates the polysemy of French loanwords in Middle English, analysing the attested senses of loan lexis in three occupational domains. Senses recorded in the Dictionnaire du moyen français and the Anglo-Norman dictionary were compared with corresponding lexical entries in the Middle English dictionary. Out of over 100 Middle English items borrowed from French, it is found that the great majority were polysemous in ways reflecting the source language semantics. This outcome contrasts with the usual finding that foreign borrowing in modern contexts produces monosemous loanwords. The conclusion is therefore drawn that the process of borrowing from French was of a different nature in the English medieval period. It is argued that bilingual users of French were agents of borrowing who replicated source word polysemy in their English. The likelihood that senses matching those of the French etymon arose in Middle English independently of source language polysemy is considered, but rejected.

Integrating Large Language Model, EEG, and Eye-Tracking for Word-Level Neural State Classification in Reading Comprehension.

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for interdisciplinary research that bridges cognitive science and natural language processing (NLP). This pilot study aims to provide insights into individuals' neural states during a semantic inference reading-comprehension task. We propose jointly analyzing LLMs, eye-gaze, and electroencephalographic (EEG) data to study how the brain processes words with varying degrees of relevance to a keyword during reading. We also use feature engineering to improve the fixation-related EEG data classification while participants read words with high versus low relevance to the keyword. The best validation accuracy in this word-level classification is over 60% across 12 subjects. Words highly relevant to the inference keyword received significantly more eye fixations per word: 1.0584 compared to 0.6576, including words with no fixations. This study represents the first attempt to classify brain states at a word level using LLM-generated labels. It provides valuable insights into human cognitive abilities and Artificial General Intelligence (AGI), and offers guidance for developing potential reading-assisted technologies.

Artificial Intelligence Techniques in Semantic Web Services

It relates to the conventional machine interaction services, is the server end of a Client-Server system relates to the data on web that is understandable by the machines, its primary objective is to permit the humans and machines together. The World Wide Web is progressing quickly in the direction of the concept of semantic language for database. The ontology tool Protégé consists of the SPARQL Query Tab to execute the queries, whereas the alternative is the Apache Jena Fuseki. The semantic web framework is comprised of different Application interfaces that interacting collectively to execute RDF data. The Fuseki supports the SPARQL queries over Hyper Text Transfer Protocol. Apache Jena Fuseki is based framework for developing Artificial Intelligence Techniques in Semantic Web Services. The proposed methodology can handle queries of various convolutions and complexities. Semantic web services on the server end provide structured and standardized information about the services they offer, typically using technologies related to ontology ensure that the data is both readable and process by machines based on ontology. The Experimental Analysis is given along with the results based on different ontology based technologies. In the disciplines of design, Thinking and implementation, along with representation ontologies are used for semantic web services.

Analysis of Irony in the Lyrics Arabic Song "A’touna Et-Tufoole" Study: Cognitive Semantics

This research examines Irony focusing on the lyrics of the song "A’touna Et-Tufoole" in Arabic version. The main objective of this research is to explore the meaning of irony in the lyrics of "A’touna Et-Tufoole" and analyze how the meaning of irony is employed within the context of the song. A qualitative approach with descriptive analysis method is employed to comprehend the meaning of irony and its descriptive qualitative implications. The research design involves content analysis of data consisting of Irony embedded in the lyrics of "A’touna Et-Tufoole" in Arabic. Data collection techniques employed in this research include reading and note-taking. The research findings encompass Verbal Irony, Dramatic Irony, and Situational Irony. This study aims to provide deeper insights into how irony functions in elucidating the meaning of songs in the Arabic language within the field of Arabic language semantics and how it might influence the understanding and interpretation of messages within different cultural contexts. Furthermore, this research is anticipated to make a significant contribution to comprehending the complex aspects of Arabic language irony.

КОМУЗ ЖАНА АНЫН БӨЛҮКТӨРҮНӨ БАЙЛАНЫШТУУ СӨЗДӨРДҮН ЛЕСИКА-СЕМАНТИКАЛЫК ӨЗГӨЧӨЛҮКТӨРҮ

The article notes the place of komuz in the life of Kyrgyz people and its role in national music. The factors of the lexeme komuz are considered. A linguistic analysis of the name of musical instrument chertme komuz and its parts waz carried out. The names of the types of national musical instruments and semantic features were also analyzed. The association of the names with imitatiwe words and the human anatomy is indicated. It is noted that the stringed komuz is one of the oldest musical instruments and under different names it has spread among other nations. It has been interpreted that komuz means not onlu a musical instrument, but also expresses the modernThe names of improved types of musical instr semantics of komuz genre komuz language, the word used to expel demons from oblivion, as well as contains an emprty decimal sound. The name ofimprowed types of musical instrument komuz are determined. The emergence of nouns "chertmek", "kol komuz" along with the word on the basis of synonymy is considered.

Spiritual-semantic approach as a methodological basis for the analysing religious music (on the example of V. Stepurko’s cycle “Monologues of the Ages”)

Spiritual-semantic approach as a methodological basis for the analysing religious music (on the example of V. Stepurko’s cycle “Monologues of the Ages”)

PRAGMATIC DEVIATIONS IN TRANSLATION OF LEXICAL BLENDS WITHIN POLITICAL DISCOURSE

The research touches upon the innovations in the English word formation processes, i.e., the role of analogy and intralingual borrowings as significant sources and ways of replenishing English vocabulary, interfering entirely with all the languages in the world and greatly influencing their development. Nominative units consisting of two or more words with a contraction of at least one of them at the place of a junction, i.e., blends, are an integral feature of the English language in general and modern English socio-political discourse in particular. Blending has been growing recently among the most productive means of word formation. Blends are needed to denote new concepts and phenomena and are often used to manifest the author’s word-formation skills; they become popular due to their expressiveness and novelty of form and content. The goal of the article is to study the functional features of blends as a means of strengthening the pragmatic component of the socio-political discourse, as well as the strategy and techniques of their translation. The general and special methods were used to achieve the goal and objectives of the study: information retrieval method – to select research material and process basic theoretical knowledge; generalization method – to highlight the most critical academic positions; deduction and induction – to clarify the theoretical foundations, generalize data and formulate conclusions; discourse analysis – to identify specific communicative and pragmatic features of socio-political communication; contextual and functional methods – to actualize the linguopragmatic meaning of the lexical units under the study, i.e., blends; the vocabulary definitions analysis – to examine their linguopragmatic peculiarities; structuralsemantic and component analysis – to determine the ways of blend formation and their main structural elements – all this is necessary for the implementation of translation analysis. The use of pragmalinguistic elements (blends, in our case) involves investigating relationships between language units and the conditions of the communicative-pragmatic space, tracing the relationship between the addressee’s intentional component and the choice of language means when translating the studied units within the socio-political discourse into another language. Conclusions. Regarding the focus of socio-political discourse on speech influence, the conveying of blend stylistics and the transfer of speech realia that form the basis of the blend implication and strengthen the socio-pragmatics of the given text cause difficulties in comprehending the English original and translation variants. The study of the translation aspect of blends within socio-political discourse revealed a lot of challenges, such as the need for a unique means for conveying blend semantics in the translation language, the complex nature of blend explication, and the problem of their interpretation. Among the translation techniques that are most effective in overcoming the outlined translation difficulties of socio-political blends, such ones should be mentioned first: transcription, transliteration, tracing, and creating an analogical model. The descriptive translation method is considered inappropriate when translating political blends (under research) because such a technique does not allow conveying pragmatics of blends and implement them in the translation language as expressive lexical and stylistic units that are components of speech influence on the addressee, and the society as a whole. The principles of the techniques studied in the article can be used for further research as being universal for translating English blends in inflectional languages (Ukrainian including), where such a phenomenon is non-typical. The process of blending hypothetically proves the activation of the redistribution of components within the different language structures and systems and their ability to self-reorganize.

How to early integrate operational diagnosis objectives in model‐driven engineering processes: A methodological proposal based on fault and behavior trees

AbstractTo help operators perform their diagnosis tasks more efficiently, the authors put forward a novel methodology introducing a new type of model, dedicated to operations, co‐created in parallel with the design models, in the preliminary stages of system development, using the language semantics of behavior trees. In this paper, the authors present the need for this new model type as expressed by the industry and justify their choice for adopting behavior trees, while illustrating in detail the proposed methodology.

USING BIG DATA IN SEMANTIC RESEARCH: PERSPECTIVES AND APPROACHES

Statement of the problem. Digitization and technology are rapidly taking over various fields of humanitarian sciences, including cognitive linguistics that is developing new algorithms and methods of language research, with linguists being encouraged to acquire new skills in data collection and processing as well as to expand their sources of language materials. In cognitive semantics and discourse studies, the biggest game-changer has been ever increasing (to the points of apparent limitlessness) volumes of various language data from digitalized texts available on the Internet that effectively accumulate in and function as Big Data – data sets that are too multiple, large and complex to be processed by traditional, ‘by-hand’ methods. Extensive language data requires specific mining and analysis tools and techniques and imposes certain restrictions on its processing and interpretation. In this paper, the authors discuss approaches to processing (‘intellectualization’) of empirical linguistic data coming from various online sources via search engines and their overall reliability for cognitive semantics research. Results. The main argument is that to ensure the validity of the digital language data under study and credible study results, researchers should employ several data collection methods and tools, i.e., opt for methodological triangulation. The authors describe their methodology on a practical example and conclude that big data systems such as indexed digital texts can effectively replace native speakers as a source of credible information on a natural language semantics as well as improve the overall quality and verifiability of semantics studies. Conclusion. Considering the national segment of the Internet in a certain language (English, Russian, etc.) as a natural text corpus of high representativeness, the researcher can use it as a tool for testing various linguistic hypotheses. At the same time, it is necessary to reflect on the intrinsic qualities of big data, such as volume, velocity (swiftness of change), variability, lack of structure, valuableness, and the presence of “noise”, as well as carefully consider, design and test procedures for using search engines to collect big data in order to ensure the quality and verifiability of the results obtained.

Generative AI in Management – Today and Tomorrow

Theoretical background: The rapid and exponential technological advancements have far-reaching impacts on management information systems, management practices, and human life. The promising outcomes in Artificial Intelligence and cutting-edge research on semantic networks and natural language processing have motivated the authors to envision the future of management technology. Purpose of the article: Our paper focuses on the new communication facilities and artificial intelligence models used to process management-type queries in natural language. Research methods: The article discusses recently developed technologies, proposed by Google and Microsoft, notably Google Bard and Bing integrated with ChatGPT-4. Both chatbots use Generative AI methods and large language models to understand domain-based queries and generate answers. Main findings: The practical and social implications of new models in management practice are discussed. To illustrate the qualities and weaknesses of the features of new technologies, four examples of management decision-making are discussed. The case studies also show differences between these two technologies. Finally, the paper concludes by summarizing the expectations and limitations of Generative AI applications in management. The paper is one of the first publications describing and demonstrating the idea of interfaces in natural language in business-oriented applications.

A cross-guidance cross-lingual model on generated parallel corpus for classical Chinese machine reading comprehension

Chinese diachronic gap is a key issue in classical Chinese machine reading comprehension (CCMRC). Preceding work on bridging this gap has been mostly restricted to limited monolingual classical Chinese corpora pre-training and lexical knowledge integration, which require a great deal of human resources. In this paper, we propose a cross-guidance cross-lingual model (CGCLM), pre-trained on a classical and modern Chinese parallel corpus generated from a large language model, to bridge the Chinese diachronic gap and reduce the manual effort. The CGCLM facilitates accurate translation by providing in-context examples and feedback based on the longest common substring between source and target sentences, thereby avoiding untranslated Chinese words. Specifically, we consider three pre-training tasks, i.e., cross-masked language modeling, linguistic label cross-prediction, and semantic cross-aware translation language modeling. The knowledge acquired from masked tokens uncovering and linguistic label predicting can lead to the implicit semantic alignment between two language styles. Taking advantage of the semantic similarity between the same syntactic levels of parallel pairs, cross-aware modeling integrates and transmits contextualized semantic information. We utilize an 18.6G monolingual corpus to create a 37.2G parallel corpus. Manual evaluation has resulted in only acceptable discrepancies between our generated and human-edited parallel corpora. Extensive experimental results show that our proposed model outperforms the state-of-the-art by an average accuracy of 3.13%, 2.44%, and 2.17% on CCMRC, classical Chinese language understanding evaluation (CCLUE), and modern Chinese language understanding evaluation (MCLUE) tasks.

Language relatedness evaluation for multilingual neural machine translation

Multilingual neural machine translation (MNMT) has attracted more and more attention in recent days because it can use a single neural machine translation (NMT) model to translate between multiple languages. As several languages are involved in MNMT, recent studies have shown that using part of these languages rather than all of them to train the model leads to comparable results. However, previous work on this topic mainly focuses on language clustering and features defined by linguists. The semantic relationship and language distance are not fully considered. How to select the most related language pairs to current low-resource pair to optimize the performance of MNMT is still an open question. In this paper, we propose to take language relatedness computation as a ranking problem, where features such as language distance, linguistic typological information and semantic relatedness features are incorporated into a random decision forest to improve the language relatedness evaluation (LRE) for MNMT. Since the model only focuses on monolingual LRE in general cross-lingual natural language processing tasks, we also propose two features related to machine translation (data size and bilingual relatedness) to predict the final language pairs. Experimental results on IWSLT and WMT datasets show that our proposed LRE method can achieve significant improvements compared with other models. We also conducted several groups of experiments on IWSLT and WMT datasets to further evaluate the effectiveness of the proposed method on MNMT. The results show that the MNMT model trained on language pairs predicted by the LRE method outperforms other language selection methods.

Association between social networking and cognition among middle aged and older adults in rural India

AbstractBackgroundEmerging evidence suggests that better social networking is associated with better cognitive performance among older adults. However, most of the prior studies have used global cognitive measures to study the above relationship. In this study, we aim to investigate the role of social networking on distinct cognitive domains, among dementia‐free middle‐aged and older‐age adults from a rural Indian population.MethodBaseline assessment data of 2403 participants (≥45 years), who are part of an ongoing, prospective, aging cohort study in rural India (Srinivaspura Aging, NeuroSenescence, and COGnition, SANSCOG) and having a Clinical Dementia Rating (CDR) of ‘0’ who are cognitively healthy was utilized for this study. Social networking was assessed using Cohen’s Social Network Index (SNI), wherein three dimensions were examined – network diversity, number of people in social network and number of embedded networks. Cognitive functions were assessed using episodic memory, geometric figure spatial recognition, verbal fluency, semantic association, word comprehension, reading comprehension and visuospatial span subtests of a culturally validated, computerized, neurocognitive test battery. Linear regression models, adjusted with age and gender were used for statistical analysis.ResultIn the linear regression model, adjusted for age and gender, higher network diversity was significantly associated with better cognitive performance in episodic memory (β = 0.16, p = 0.001), attention function (β = 0.07, p = 0.004), geometric figure spatial recognition (β = 0.13, p = 0.001), verbal fluency phonemic cue (β = 0.11, p = 0.001), reading comprehension (β = 0.09, p = 0.001) and semantic language association function (β = 0.08, p = 0.001). Further, higher dimension of number of people in social network was significantly associated with better cognitive performances in semantic language association function (β = 0.002, p = 0.04). Furthermore, in the third dimension of SNI, higher dimension of number of embedded networks was significantly associated with better cognitive performances in visuospatial span (β = 0.05, p = 0.046) and verbal fluency phonemic cue (β = 0.103, p = 0.001).ConclusionThe findings suggest that better social networking in all three dimensions was associated with better cognitive functions in multiple distinct cognitive domains. It is important to follow‐up these participants longitudinally with serial cognitive monitoring to understand if social networking can potentially delay cognitive decline and thereby, serve as a protective factor for dementia.