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The paper reveals the main parameters of language modeling of emotions in the literary text, such as causation, temporal localization, intensity, external manifestation. Using the material of stories of the collection Dark alleys by I.A. Bunin, we analyze lexical and phraseological designates of emotion of fear which specify these parameters. Thus, the parameter of the causality of fear combines such particular characteristics as the 'objectivity of emotion', 'internal / external causation of emotion', 'connection with the intellectual and volitional spheres', etc.; time parameter – 'temporal actuality (episodicity) / timelessness (stability) of emotion', 'static / dynamic emotion', 'phasic flow of emotion', etc. It is revealed that I.A. Bunin's prose emphasizes not only individual aspects of emotions but also specific signs within these aspects. Thus, when portraying fear, the parameter of the external expression of emotion takes the leading place, and inside it – the sign 'physiological symptomatology'. The peculiarity of the linguistic expression of these parameters is determined, on the one hand, by the systemic relations of the emotions, which are projected onto the semantic space of the text, and, on the other hand, by the associative links of these units within the specific works. In the prose of I.A. Bunin fear is described not only as a simple emotional situation, but also as a component of complex emotions, denoted by oxymorons such as the rapture of horror, fear of happiness, etc. Due to the use of such multicomponent names, the boundaries of the emotional world of heroes are widened, and the possibility of "combining" different experiences is emphasized.

The article is an essay on the development of technologies for natural language processing, which formed the basis of BERT (Bidirectional Encoder Representations from Transformers), a language model from Google, showing high results on the whole class of problems associated with the understanding of natural language. Two key ideas implemented in BERT are knowledge transfer and attention mechanism. The model is designed to solve two problems on a large unlabeled data set and can reuse the identified language patterns for effective learning for a specific text processing problem. Architecture Transformer is based on the attention mechanism, i.e. it involves evaluation of relationships between input data tokens. In addition, the article notes strengths and weaknesses of BERT and the directions for further model improvement.

First developed in 2018 by Google researchers, Bidirectional Encoder Representations from Transformers (BERT) represents a breakthrough in natural language processing (NLP). BERT achieved state-of-the-art results across a range of NLP tasks while using a single transformer-based neural network architecture. This work reviews BERT's technical approach, performance when published, and significant research impact since release. We provide background on BERT's foundations like transformer encoders and transfer learning from universal language models. Core technical innovations include deeply bidirectional conditioning and a masked language modeling objective during BERT's unsupervised pretraining phase. For evaluation, BERT was fine-tuned and tested on eleven NLP tasks ranging from question answering to sentiment analysis via the GLUE benchmark, achieving new state-of-the-art results. Additionally, this work analyzes BERT's immense research influence as an accessible technique surpassing specialized models. BERT catalyzed adoption of pretraining and transfer learning for NLP. Quantitatively, over 10,000 papers have extended BERT and it is integrated widely across industry applications. Future directions based on BERT scale towards billions of parameters and multilingual representations. In summary, this work reviews the method, performance, impact and future outlook for BERT as a foundational NLP technique. We provide background on BERT's foundations like transformer encoders and transfer learning from universal language models. Core technical innovations include deeply bidirectional conditioning and a masked language modeling objective during BERT's unsupervised pretraining phase. For evaluation, BERT was fine-tuned and tested on eleven NLP tasks ranging from question answering to sentiment analysis via the GLUE benchmark, achieving new state-of-the-art results. Additionally, this work analyzes BERT's immense research influence as an accessible technique surpassing specialized models. BERT catalyzed adoption of pretraining and transfer learning for NLP. Quantitatively, over 10,000 papers have extended BERT and it is integrated widely across industry applications. Future directions based on BERT scale towards billions of parameters and multilingual representations. In summary, this work reviews the method, performance, impact and future outlook for BERT as a foundational NLP technique.

Transformer-based language models have revolutionized Natural Language Processing tasks, with advancements in language modeling techniques. Current transformer architectures utilize attention mechanisms to model text dependencies effectively. Studies have shown that these models embed syntactic structures and knowledge, explaining their performance in tasks involving syntactic and semantic elements. However, transformer-based models are prone to hallucination where incorporated knowledge is not utilized effectively. To address this, methods are emerging to mitigate hallucination and integrate external knowledge sources like knowledge graphs (e.g., Freebase, WordNet, ConceptNet, ATOMIC). Knowledge graphs represent real-world knowledge through entities and relationships offering a potential injection point to enhance model performance in inference tasks. Various injection approaches, including input, architectural, and output injections, aim to incorporate knowledge from graphs into transformer models. Input injections modify data preprocessing, architectural injections add layers for knowledge integration, and output injections adjust error functions to correct knowledge incorporation during training. Despite ongoing research, a universal solution to hallucination remains elusive, and a standardized benchmark for comparing injection methods is lacking. This study investigates knowledge graphs as one of the methods to mitigate hallucination and their possible integration into Large Language Models. Comparative experiments across General Language Understanding Evaluation benchmark tasks demonstrated that ERNIE 3.0 and XLNet outperform other injection methods with the average scores of 91.1 % and 90.1 %.

Copyright of Bylye Gody is the property of Cherkas Global University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The problem of building a computer model of a small language was under solution. The relevance of this task is due to the following considerations: the need to eliminate the information inequality between speakers of different languages; the need for new tools for the study of poorly understood languages, as well as innovative approaches to language modeling in the low-resource context; the problem of supporting and developing small languages.There are three main objectives in solving the problem of small natural language processing at the stage of describing the problem situation: to justify the problem of modeling language in the context of resource scarcity as a special task in the field of natural languages processing, to review the literature on the relevant topic, to develop the concept of language acquisition model with a relatively small number of available resources. Computer modeling techniques using neural networks, semi-supervised learning and reinforcement learning were involved.The paper provides a review of the literature on modeling the learning of vocabulary, morphology, and grammar of a child's native language. Based on the current understanding of the language acquisition and existing computer models of this process, the architecture of the system of small language processing, which is taught through modeling of ontogenesis, is proposed. The main components of the system and the principles of their interaction are highlighted. The system is based on a module built on the basis of modern dialogical language models and taught in some rich-resources language (e.g., English). During training, an intermediate layer is used which represents statements in some abstract form, for example, in the symbols of formal semantics. The relationship between the formal recording of utterances and their translation into the target low-resource language is learned by modeling the child's acquisition of vocabulary and grammar of the language. One of components stands for the non-linguistic context in which language learning takes place.This article explores the problem of modeling small languages. A detailed substantiation of the relevance of modeling small languages is given: the social significance of the problem is noted, the benefits for linguistics, ethnography, ethnology and cultural anthropology are shown. The ineffectiveness of approaches applied to large languages in conditions of a lack of resources is noted. A model of language learning by means of ontogenesis simulation is proposed, which is based both on the results obtained in the field of computer modeling and on the data of psycholinguistics.

Copyright of Stratum Plus Journal is the property of P.P. Stratum plus and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article deals with the problem of the formation of major subject competency of future primary school teachers to teach fine arts in a foreign language. The research methods used during the work were the following: analysis of pedagogical, psychological, didactic literature on the research problem, which allows us to carry out a theoretical substantiation for the feasibility of forming a major subject competency of future primary school teachers to teach fine arts in a foreign language; modeling the educational process. The essence of the major subject competency of future primary school teachers, as well as its components such as: artistic and visual competency within the discipline “Fine Arts” which includes: historical and artistic, genre and artistic, technical and graphic competences, and foreign language and speech competency within the discipline “Foreign language” which embraces linguistic competence and speech competence, have been determined. A model of the process of the formation of the major subject competency of future primary school teachers to teach such a compulsory school subject as fine arts in a foreign language has been elaborated. The model includes 5 stages: motivational, which is carried out by means of a foreign language; receptive, which consists in presenting students with a new thematic material in the form of lectures; reproductive, which consists in the development of skills to retell the previously listened to lecture material in the oral and written forms; productive, which consists in teaching students: to discuss the contents of the lecture, to interpret and express their points of view regarding the content of the lecture, to expand independently its content and make a presentation; reflective and creative, which consists in the development of skills of practical application of the acquired knowledge in a foreign language

The article studies the interaction of the system of language through its modeling by means of the three level phenomenon of language personality, which determines language modeling and thus forms the world view.

The article addresses methodological modeling as an essential element in interactive Russian language teaching. Educational modeling is regarded both as a method and a teaching tool. The author provides detailed analysis of methodological modeling and its types that can be applied to interactive instruction. The research also involves comparison of modeling as a productive teaching method in teaching Russian to native speakers and teaching it as a foreign language. The results of this comparative research show a definite potential of modeling as a method of interactive teaching and learning. The research demonstrates the advantages of interactive instruction when semantic modeling is used in teaching Russian as a FL. Syntactic modeling in interactive instruction also opens new possibilities in mastering grammatical synonymy by native learners of Russian. The article concludes that it is advisable and imperative, to use the modeling method within the interactive instruction framework, if the target learners' contingent is properly considered; the advantages of this method and its specifics are clearly shown in teaching the Russian language both to native speakers and to foreigners. [ABSTRACT FROM AUTHOR]