  Hindi being a highly inflectional language, FST (Finite State Transducer)
based approach is most efficient for developing a morphological analyzer for
this language. The work presented in this paper uses the SFST (Stuttgart Finite
State Transducer) tool for generating the FST. A lexicon of root words is
created. Rules are then added for generating inflectional and derivational
words from these root words. The Morph Analyzer developed was used in a Part Of
Speech (POS) Tagger based on Stanford POS Tagger. The system was first trained
using a manually tagged corpus and MAXENT (Maximum Entropy) approach of
Stanford POS tagger was then used for tagging input sentences. The
morphological analyzer gives approximately 97% correct results. POS tagger
gives an accuracy of approximately 87% for the sentences that have the words
known to the trained model file, and 80% accuracy for the sentences that have
the words unknown to the trained model file.
