ADVANCED TRANSLATION OF SCIENTIFIC TERMINOLOGY USING COMPUTATIONAL LINGUISTICS: THEORY, PRACTICE, AND LIMITATIONS

Аннотация

В данной статье рассматриваются теоретические и практические аспекты использования средств компьютерной лингвистики при переводе научной терминологии. Анализируются возможности и ограничения машинного перевода (MT), инструментов компьютерной поддержки перевода (CAT) и терминологических баз данных. На основе теорий Ньюмарка, Вине и Дарбельне, а также Вермеера предлагаются современные подходы к адекватному переводу терминов в контексте. Сравнительный анализ демонстрирует различия между инструментами перевода, предлагая гибридные стратегии для повышения качества перевода. Ключевые слова: компьютерная лингвистика, научная терминология, машинный перевод, теория перевода, контекстуальные проблемы, память перевода. https://scientific-jl.com/new 22 22 88 Volume–80_Issue-1_July-2025 JOURNAL OF NEW CENTURY INNOVATIONS The globalization of science necessitates accurate and context-sensitive translation of scientific terminology across languages. The complexity of domain- specific vocabulary often presents challenges for both human and machine translators. Computational linguistics has introduced powerful tools, but the quality of output remains uneven, especially in fields requiring precision such as medicine, law, and engineering (Newmark, 1988). This paper investigates the current capabilities of such tools and offers a theoretically grounded approach to their integration into professional workflows. Translation theory has evolved to address not only linguistic transfer but also contextual, pragmatic, and cultural dimensions. Newmark (1988) differentiates between semantic translation (focused on meaning) and communicative translation (focused on effect). Vinay and Darbelnet (1958) identify translation techniques such as borrowing, calque, and modulation, essential for adapting technical terminology. Vermeer’s Skopos theory (1989) emphasizes the purpose of translation as central to determining strategy, particularly in scientific communication where accuracy outweighs stylistic preferences. Classification of Computational Linguistic Tools Scientific terminology translation increasingly depends on three categories of tools: Machine Translation (MT): e.g., Google Translate, DeepL Computer-Assisted Translation (CAT): e.g., SDL Trados, MemoQ, Smartcat Terminological Databases: e.g., IATE, Termium, UNTerm MT systems like Google Translate provide rapid outputs, but as Hutchins (2005) notes, they lack contextual nuance. CAT tools enhance consistency using translation memory (TM) and terminology management. Databases such as IATE provide validated equivalents to ensure terminological uniformity (Bowker & Fisher, 2010). Main Challenges in Terminology Translation Despite advancements, several challenges persist: Polysemy: Terms such as “network” vary in meaning across disciplines (e.g., IT vs. biology). Cultural absence: Concepts like “peer review” may lack direct equivalents in certain cultures. Semantic drift: MT systems may dilute precise meaning (e.g., “antibiotic resistance” translated as “qarshilik” instead of “rezistentlik”). Inconsistency: Different tools yield varied outputs for identical terms (Koehn, 2020). https://scientific-jl.com/new 22 22 99 Volume–80_Issue-1_July-2025 JOURNAL OF NEW CENTURY INNOVATIONS Practical Comparison of Tools Term Google translate DeepL SDL Trados Cloud computing Bulutli hisoblash Bulut asosida Bulutli hisoblash texnologiyalar asosidagi hisoblash Antibiotic Antibiotik Antibiotikga Antibiotikga resistance qarshiligi chidamlilik rezistentlik Emission control Emissiya nazorati Emissiya Chiqarilayotgan system tizimi boshqaruv tizimi gazlarni boshqarish tizimi As shown in the table, SDL Trados—especially when supported by subject- specific TMs—offers more nuanced and contextually accurate translations. The phrase “Emission control system” is particularly well-rendered in Trados, as it reflects the terminology used in environmental and automotive documentation (Bowker & Fisher, 2010). Analysis of Comparative Results As demonstrated in the table, automated translation systems—particularly Google Translate and DeepL—offer rapid and generally intelligible outputs; however, they fall short in domains that demand terminological precision and contextual accuracy. This observation aligns with Newmark's (1988) assertion that translation is not merely linguistic substitution but involves complex semantic and pragmatic transfer. For instance, the term "antibiotic resistance", when rendered as "qarshilik" (resistance), may suffice in general discourse but fails to convey the technical nuance intended by "rezistentlik" in Uzbek medical terminology. Moreover, while DeepL occasionally offers context-sensitive suggestions superior to Google Translate, it often exhibits a tendency toward stylistic simplification or semantic generalization. A good example is the translation of "cloud computing" as "bulut asosida hisoblash" (computing based on clouds), which is grammatically correct but not widely used or recogn