POS Tagging for Marathi Language using Hidden Markov Model
DOI:
https://doi.org/10.26438/ijcse/v6i1.409412Keywords:
Marathi, HMM, POS, Part of Speech, Tagset, Supervised learningAbstract
Part-of-speech (POS) tagging plays significant role in almost every natural language processing task. This paper addresses a problem of POS tagging for Marathi language. Marathi is free word order, morphologically rich and highly inflectional Indian language. Supervised learning method that uses Hidden Markov Model is implemented to mark Marathi text using POS tags. The dataset required for training the algorithm consists of 12,000 Marathi sentences comprising news from popular Marathi newspaper. The algorithm for POS tagging predicts the tag for current word using the previous word tag pair. The POS tagging system has reported 86.61% accuracy in predicting correct POS to the words.
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