Analyzing the syntactic and semantic specifics of AI-assisted Uzbek–English written translation editing

Нилуфар, Курбанова

Lingvospektr · 2025-yil

Annotatsiya

This paper analyzes the syntactic and semantic features of Uzbek-English translations generated by artificial intelligence (AI) tools. It investigates how machine-translated texts differ from human translations in sentence structure, lexical selection, and semantic coherence. Particular focus is placed on syntactic deviations, shifts in word order, and contextual inaccuracies frequently found in AI-generated output. Using comparative analysis of translations produced by Google Translate and ChatGPT, the study identifies patterns of grammatical simplification, semantic ambiguity, and stylistic inconsistency. The results show that while AI systems can generate structurally acceptable translations, they often lack nuanced meaning and cultural appropriateness. These deficiencies underscore the crucial role of human post-editing in improving fluency, contextual relevance, and communicative precision. Ultimately, the study contributes to a better understanding of AI-assisted translation dynamics between Uzbek and English and emphasizes the continued need for human expertise in achieving high-quality, culturally sensitive translation outcomes. Furthermore, the research highlights the pedagogical value of AI translation tools for language learning, emphasizing their potential as supplementary aids rather than replacements for human expertise in professional translation.

Maqola ma’lumotlari
MualliflarНилуфар, Курбанова
JurnalLingvospektr
Nashr sanasi2025-11-05
Jild10
Son1
Betlar271-278
TilIngliz

Kalit so‘zlar

Перевод с ИИ, узбекско–английский перевод, синтаксис, семантика, постредактирование, качество машинного перевода, языковая эквивалентность, SI tarjimasi, o‘zbek-ingliz tarjimasi, sintaksis, semantika, tahrirdan keyingi qayta ishlash, mashina tarjimasi sifati, lingvistik ekvivalentlik, AI translation, Uzbek–English translation, syntax, semantics, post-editing, machine translation quality, linguistic equivalence

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