AI-ASSISTED LINGUISTIC AND SEMANTIC MODELING OF AVIATION RADIOTELEPHONY DISCOURSE IN ENGLISH AND UZBEK

Abstract

The growing integration of artificial intelligence (AI) technologies into linguistic research has created new opportunities for analyzing specialized professional discourse. Aviation radiotelephony communication represents a highly standardized form of professional interaction in which linguistic precision and semantic clarity are essential for ensuring flight safety. This study investigates the linguistic and semantic characteristics of aviation radiotelephony discourse in English and Uzbek through AI-assisted modeling techniques. The research focuses on the identification of discourse structures, terminological patterns, communicative functions, and semantic relationships within radiotelephony communication. Using natural language processing (NLP) methods, aviation phraseology and radiotelephony exchanges were analyzed to identify similarities and differences between the two languages. The findings indicate that both English and Uzbek aviation discourse exhibit a high degree of standardization, brevity, and operational efficiency. However, differences emerge in lexical realization, syntactic organization, and semantic adaptation. The study highlights the potential of AI-based linguistic modeling for improving aviation communication training, terminology management, and multilingual aviation safety systems.