PART OF SPEECH TAGGING AND ITS APPLICATIONS: AN IMRAD-STRUCTURED RESEARCH ARTICLE

Abstract

ABSTRACT: Part-of-speech (POS) tagging is a core procedure in Natural Language Processing (NLP) that assigns grammatical categories to words in a text. This study provides a comprehensive analysis of POS tagging using the IMRaD structure, examining its theoretical foundations, methodological approaches, and practical applications. In the Methods section, the main tagging approaches—rule-based systems, statistical models, machine learning algorithms, and deep learning architectures—are reviewed in detail. The Results summarize the impact of these methods on computational linguistics tasks such as parsing, sentiment analysis, machine translation, and information extraction. The Discussion highlights challenges related to ambiguity, morphology, and multilingual data, offering insights into future improvements driven by large language models. The paper concludes that POS tagging remains a fundamental step for linguistic analysis and technological applications, and its effectiveness continues to grow with advancements in artificial intelligence.