  Part-of-speech (POS) tagging is a process of assigning the words in a text
corresponding to a particular part of speech. A fundamental version of POS
tagging is the identification of words as nouns, verbs, adjectives etc. For
processing natural languages, Part of Speech tagging is a prominent tool. It is
one of the simplest as well as most constant and statistical model for many NLP
applications. POS Tagging is an initial stage of linguistics, text analysis
like information retrieval, machine translator, text to speech synthesis,
information extraction etc. In POS Tagging we assign a Part of Speech tag to
each word in a sentence and literature. Various approaches have been proposed
to implement POS taggers. In this paper we present a Marathi part of speech
tagger. It is morphologically rich language. Marathi is spoken by the native
people of Maharashtra. The general approach used for development of tagger is
statistical using Unigram, Bigram, Trigram and HMM Methods. It presents a clear
idea about all the algorithms with suitable examples. It also introduces a tag
set for Marathi which can be used for tagging Marathi text. In this paper we
have shown the development of the tagger as well as compared to check the
accuracy of taggers output. The three Marathi POS taggers viz. Unigram, Bigram,
Trigram and HMM gives the accuracy of 77.38%, 90.30%, 91.46% and 93.82%
respectively.
