A practical part-of-speech tagger for Bengali

This paper presents a practical part-of-speech (POS) tagger for Bengali, which will accept a raw Bengali text (typed in Bengali font) to produce a Bengali POS tagged output which can be directly used for other NLP applications. We have implemented a supervised Bengali trigram POS Tagger from the scratch using a statistical machine learning technique that uses the second order Hidden Markov Model (HMM). We have considered the bigram POS tagger as the baseline tagger to which our developed trigram POS tagger has been compared.