Comparison of Generative and Discriminative Models of Part of Speech Taggers for Marathi Language - Deshmukh, Rushali Dhumal (International Journal of Computer Sciences and Engineering, 2025)
Part of Speech (POS) tagging is the process of assigning grammatical category to words. POS tagger has wide variety of applications in the field of natural language processing, speech processing, information retrieval, machine translation, sentiment analysis, question answering etc. For Indian languages, the research in the field of POS tagging is still in progress. Marathi is the fourth spoken language in India and morphologically rich language. In this paper, we compared performance of Marathi POS tagger using generative and discriminative models. Using 32 tags, specified by Unified POS standard for Marathi, POS tagged dataset of 1500 news sentences, from different domains like sports, politics, entertainment etc., is generated. The Naive Bayes, Decision Tree, Neural Network, K Nearest Neighbour, Hidden Markov Model and Conditional Random Fields give 81%, 79%, 85%, 78%, 79% and 86% accuracy on test data respectively. Results show that neural network and Conditional Random Fields give better performance