PART-OF-SPEECH TAGGING AND ITS APPLICATIONS (Ustozlar uchun, 2025)
This article investigates the theoretical foundations, methodologies, and applications of part-of-speech (POS) tagging within the framework of corpus linguistics. POS tagging, as a fundamental stage of natural language processing (NLP), enables computational systems to identify and classify words into grammatical categories, thereby providing a structural basis for syntactic and semantic analysis. The study reviews the evolution of POS tagging approaches—from rule-based to stochastic, hybrid, and deep learning models—and emphasizes their role in linguistic annotation and empirical language research. Using corpus-based examples, the research explores how accurate POS tagging enhances tasks such as parsing, information retrieval, text classification, and machine translation. The discussion highlights challenges specific to morphologically rich and low-resource languages, including Kazakh and Uzbek, and outlines strategies for building effective language resources. The study concludes that POS tagging is not only a technical process but also a methodological instrument for linguistic inquiry, linking computational technology with linguistic theory.