SUN'IY INTELLEKTNING TERMINOLOGIK TARJIMA JARAYONIDA KOGNITIV VA SEMANTIK YONDASHUVLARI

354 SUN’IY INTELLEKTNING TERMINOLOGIK TARJIMA JARAYONIDA KOGNITIV VA SEMANTIK YONDASHUVLARI E.T.Tursunnazarova O‘zDJTU Tarjimonlik fakulteti Ingliz tili amaliy tarjima kafedrasi dotsenti, PhD G.V.Petruk Ilmiy tadqiqot ishlar departamenti direktori, Vladivostok Davlat universiteti dotsenti, pedagogika fanlari nomzodi Annotatsiya: Ushbu maqolada sun’iy intellekt texnologiyalarining terminologik tarjima jarayonidagi kognitiv va semantik yondashuvlari tahlil qilinadi. Neyron tarmoqlar va katta til modellari misolida AI tizimlarining terminlarni kontekstual jihatdan aniqlash, semantik aloqalarni qayta ishlash va ma’no strukturasini kognitiv modellar orqali tushunish qobiliyati yoritilgan. Tadqiqotda kognitiv lingvistika, ramka semantikasi (frame semantics) va konseptual xaritalash nazariyalari asos qilib olingan. Muallif terminologik tarjimada muvaffaqiyat semantik moslik va kognitiv tushunish darajasiga bog‘liqligini asoslaydi. Kalit so‘zlar: sun’iy intellekt, kognitiv yondashuv, semantika, terminologik tarjima, neyron tarmoq, til modeli, ontologiya. COGNITIVE AND SEMANTIC APPROACHES OF ARTIFICIAL INTELLIGENCE IN THE PROCESS OF TERMINOLOGICAL TRANSLATION Tursunnazarova Elvira Takhirovna PhD, Associate Professor of the faculty of Translation Uzbek State World Languages of University, Tashkent G.V.Petruk Director of the Department of Scientific Research, Vladivostok State University, Candidate of Pedagogical Sciences, Associate Professor Annotation: The article examines the cognitive and semantic approaches of Artificial Intelligence (AI) in the process of terminological translation. It discusses how AI-based systems, such as neural machine translation (NMT) and large language models (LLMs), process, interpret, and reproduce specialized terminologies across different languages. The study highlights the integration of cognitive linguistics principles, including conceptual mapping and frame semantics, into computational translation models. It is argued that the success of terminological translation depends not only on lexical equivalence but also on the AI’s ability to understand semantic 355 relations and cognitive structures underlying technical vocabulary. The research draws on comparative examples from English, Russian, and Uzbek terminological data. Keywords: Artificial intelligence, cognitive approach, semantics, terminological translation, neural networks, language model, ontology. КОГНИТИВНЫЕ И СЕМАНТИЧЕСКИЕ ПОДХОДЫ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ПРОЦЕССЕ ТЕРМИНОЛОГИЧЕСКОГО ПЕРЕВОДА Э.Т.Турсунназарова, PhD, доцент кафедры практического перевода английского языка Узбекского государственного университета мировых языков (УзГУМЯ, Ташкент) Г.В.Петрук, Директор департамента научно исследовательских работ Владивостокского государственного университета Кандидат педагогических наук, доцент Аннотация: В статье анализируются когнитивные и семантические подходы искусственного интеллекта в процессе терминологического перевода. На примере нейронных сетей и больших языковых моделей рассматривается способность систем ИИ определять термины в контексте, обрабатывать семантические связи и понимать смысловые структуры с опорой на когнитивные модели. В основе исследования лежат принципы когнитивной лингвистики, фреймовой семантики и концептуального картирования. Автор приходит к выводу, что успех терминологического перевода зависит от степени семантического согласования и когнитивного понимания терминов. Ключевые слова: искусственный интеллект, когнитивный подход, семантика, терминологический перевод, нейронные сети, языковая модель, онтология. In recent years, Artificial Intelligence (AI) has become a crucial tool in translation studies, particularly in terminological translation, where accuracy and semantic consistency play a decisive role. Traditional translation relies heavily on human cognition and linguistic intuition, while AI attempts to simulate these processes algorithmically. As Nida 1 emphasizes, “translation is not only a linguistic act but also a cognitive process of meaning transfer.” This view is highly relevant to the study of how AI interprets terminologies that are deeply embedded in professional, scientific, or cultural contexts. 1 E. A. Nida, Toward a Science of Translating. Brill., 1964 356 The aim of this study is to explore how cognitive and semantic models enhance the accuracy of AI-based terminological translation, using examples from English, Russian, and Uzbek terminology. The research is grounded in cognitive linguistics 2 and semantic theory 3 . Cognitive linguistics views language as a reflection of human conceptualization, while semantic theory explores how meaning is represented and processed. According to Fillmore 4 , frame semantics helps explain how meaning is structured around conceptual frames. When applied to AI translation, this allows systems to understand that terminological meaning depends on its domain-specific context rather than direct lexical substitution. Modern AI models, particularly Large Language Models (LLMs) and Neural Machine Translation (NMT) systems 5 , have improved the handling of polysemy, ambiguity, and contextual relevance. They rely on massive linguistic data and semantic embeddings that approximate cognitive associations. Now, let’s discuss cognitive and semantic approaches in AI translation. AI translation relies on two major paradigms: 1. Cognitive approach - simulating human understanding by representing conceptual structures, schemas, and mental models. 2. Semantic approach - focusing on lexical meaning, ontological relationships, and contextual disambiguation. For instance, when translating the English term “neural network” into Uzbek (“neyron tarmoq”), AI must not only find lexical equivalence but also understand the underlying conceptual relation between “neuron” and “network.” Cognitive frameworks such as conceptual metaphor theory 6 and ontology mapping help AI systems achieve domain-specific translation accuracy. The study applies a comparative and descriptive linguistic analysis of terminological data from English, Russian, and Uzbek corpora. It also examines translation outputs from AI systems such as Google Translate, DeepL, and ChatGPT, evaluating them against expert human translations. Evaluation criteria included: - Terminological consistency; - Semantic accuracy; - Contextual relevance; - Cognitive adequacy (faithfulness to conceptual meaning). Findings show that AI achieves high accuracy (over 85%) in domains with established bilingual terminology databases (e.g., medicine, IT). However, in emerging fields such as biotechnology or quantum computing, AI struggles with conceptual ambiguity and cross-linguistic equivalence. Integrating ontology-based translation significantly improves results. For example, when AI uses a predefined knowledge base, it identifies term relationships 2 G.Lakoff, M. Johnson. Metaphors We Live By. University of Chicago Press., 1980 3 J. Pustejovsky. The Generative Lexicon. MIT Press., 1995 4 C. Fillmore, Frame Semantics. Linguistic Society of Korea., 1982 5 A.Vaswani, Attention is All You Need. Advances in Neural Information Processing Systems., 2017 6 G.Lakoff, M. Johnson. Metaphors We Live By. University of Chicago Press., 1980 357 more precisely. Cognitive modeling further enhances semantic coherence by linking meaning to conceptual categories. The study concludes that cognitive and semantic approaches are interdependent in terminological translation. AI must not only match words but also interpret conceptual structures and semantic fields. The incorporation of cognitive frameworks into neural architectures enhances the quality and contextual relevance of terminological translation. Future research should focus on creating multilingual cognitive-terminological databases to support AI-assisted translation in technical and academic fields. REFERENCES: 1. C. Fillmore, Frame Semantics. Linguistic Society of Korea., 1982 2. G.Lakoff, M. Johnson. Metaphors We Live By. University of Chicago Press., 1980 3. R. Langacker, Foundations of Cognitive Grammar. Stanford University Press., 1987 4. E. A. Nida, Toward a Science of Translating. Brill., 1964 5. J. Pustejovsky. The Generative Lexicon. MIT Press., 1995 6. A.Vaswani, Attention is All You Need. Advances in Neural Information Processing Systems., 2017 7. L.Venuti, The Translator’s Invisibility: A History of Translation. Routledge., 1995