WordNet as a Model for Developing a Bilingual Thesaurus of English and Uzbek Qualitative Adjectives
Year: 2026
Type: conference-paper
Abstract: An analytical review of WordNet thesauri explores the development, structure, and applications of WordNet, a pioneering lexical database initiated at Princeton University in 1985. Unlike traditional thesauri, WordNet organizes words into semantic networks known as synsets, grouping terms based on shared meanings and relationships to provide a deeper understanding of language. This innovative structure has established WordNet as a key resource in natural language processing (NLP), supporting advances in semantic analysis, word sense disambiguation, and machine learning. WordNet’s model has influenced various applications, including information retrieval, sentiment analysis, and machine translation. Building on these principles, this study examines the use of WordNet as a model for developing a bilingual thesaurus of English and Uzbek qualitative adjectives. A central focus is the analysis of the Uzbek adjective o’tkir, where collocations, synonyms, and antonyms are mapped to English equivalents. This bilingual approach enhances semantic precision and contributes to the expansion of the Uzbek corpus. By adopting WordNet’s relational structure, the proposed thesaurus supports cross-linguistic semantic analysis and enriches resources for multilingual NLP tasks. The study concludes with recommendations for adapting semantic networks to language-specific contexts to improve accuracy and usability in computational linguistics.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7224939618