IMPLEMENTATION OF HIDDEN MARKOV MODEL (HMM) FOR PARTS OF SPEECH TAGGING IN TELUGU LANGUAGE

All NLP applications have fundamental task of POS(Parts of Speech) Tagging. Like Grammar Checking, Speech processing, Machine translation etc. that assign the correct tag to the word for a number of available tags. The accuracy of a tagger is the biggest challenge today. A lot of taggers have been proposed by different Researchers for the different languages (Telugu, Tamil, Kannada, Punjabi, Hindi, Bengali etc.) using different techniques like HMM (Hidden Markov Model), SVM (Support Vector Machine), ME (Maximum Entropy) etc. A Telugu POS tagger based on HMM model is one of them. This tagger uses Hidden Markov Model., a statistical technique to accurately tag the words in Telugu language using 630 tags developed by Rama Sree, R.J, Kusuma Kumari,2007.This large tag set (630 tags)results in data sparseness problem. Finally the result has been manually evaluated from a linguistic person. To cope up with this problem, in this research paper an experiment with reduced POS Tag set (36 tags) proposed by Technical Development of Indian Languages (TDIL) has been used to improve the tagging accuracy of HMM based POS Tagger