COMPARATIVE CORPUS-BASED ANALYSIS OF AI TERMINOLOGY IN ENGLISH AND UZBEK

Abstract

This study investigates the corpus-based comparative features of artificial intelligence (AI) terminology in English and Uzbek, focusing on their semantic, structural, and functional properties. Using data extracted from contemporary linguistic corpora, the research analyzes the frequency patterns, collocational profiles, and contextual behavior of key AI terms such as machine learning, neural network, and natural language processing, along with their Uzbek counterparts mashinali o‘qitish, neyron tarmoq, and tabiiy tilni qayta ishlash. The study also identifies major tendencies in term formation, including borrowing, calquing, and morphological adaptation that shape the development of Uzbek AI terminology. Comparative corpus evidence reveals both convergence and divergence in meaning extension, syntagmatic relations, and discourse-specific functions across the two languages. The findings contribute to a deeper understanding of cross-linguistic terminology evolution and provide practical implications for translation studies, lexicography, and AI-related language policy in Uzbekistan.