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Hindi POS Tagger Using Naive Stemming : Harnessing Morphological Information Without Extensive Linguistic Knowledge

Part of Speech tagging for Indian Languages in general and Hindi in particular is not a very widely explored territory. There have been many attempts at developing a good POS tagger for Hindi, but the morphological complexity of the language makes it a hard nut to crack. Some of the best taggers available for Indian Languages employ hybrids of machine learning or stochastic methods and linguistic knowledge. Though, the results achieved using such methods are good, there practicability for other inflective Indian Languages is reduced due to their heavy dependence on linguistic knowledge. Even though taggers can achieve very good results if provided good morphological information, the cost of creating these resources renders such methods impractical. In this paper, we present a simple HMM based POS tagger, which employs a naive(longest suffix matching) stemmer as a pre-processor to achieve reasonably good accuracy of 93.12%. This method does not require any linguistic resource apart from a list of possible suffixes for the language. This list can be easily created using existing machine learning techniques. The aim of this method is to demonstrate that even without employing tools like morphological analyzer or resources like a pre-compiled structured lexicon, it is possible to harness the morphological richness of Indian Languages.