A Trigram HMM-Based POS Tagger for Indian Languages

We present in this paper a trigram HMM-based (Hidden Markov Model) part-of-speech (POS) tagger for Indian languages, which will accept a raw text in an Indian language (typed in corresponding language font) to produce a POS tagged output. We implement the trigram POS Tagger from the scratch based on the second order Hidden Markov Model (HMM). For handling unknown words, we introduce a prefix analysis method and a word-type analysis method which are combined with the well known suffix analysis method for predicting the probable tags. Though our developed systems have been tested on the data for four Indian languages namely Bengali, Hindi, Marathi and Telugu, the developed system can be easily ported to a new language just by replacing the training file with the POS tagged data for the new language. Our developed trigram POS tagger has been compared to the bigram POS tagger defined as a baseline.