Exploring The Potential of HMMs in Linguistics for Part of Speech (POS) Tagging

The exponential growth in the adoption of information and communication technologies has sparked a notable surge in the demand for Natural Language Processing (NLP) tools. The tagging/identification of Part-of-Speech (POS) is of utmost importance in numerous natural language processing applications, including information extraction, parsing, and machine translation. It entails the assignment of a distinct part of speech to every word within a given corpus. The Hidden Markov Model (HMM) stands as the prevailing technique employed for Part-of-Speech tagging. The foundation of this approach lies in a probabilistic model that effectively captures the intricate relationships between words and their corresponding tags in a sequential manner. In this research, the Hidden Markov Model (HMM) is investigated for its potential application in POS tagging. It showcases the dependability and efficacy of this approach by attaining an impressive accuracy rate of 96.67%. Also, the precision, recall, and F-score metrics achieved stand at 96%, 96.5%, and 95% respectively, further solidifying the effectiveness of the HMM method.