  A word having multiple senses in a text introduces the lexical semantic task
to find out which particular sense is appropriate for the given context. One
such task is Word sense disambiguation which refers to the identification of
the most appropriate meaning of the polysemous word in a given context using
computational algorithms. The language processing research in Hindi, the
official language of India, and other Indian languages is restricted by
unavailability of the standard corpus. For Hindi word sense disambiguation
also, the large corpus is not available. In this work, we prepared the text
containing new senses of certain words leading to the enrichment of the
sense-tagged Hindi corpus of sixty polysemous words. Furthermore, we analyzed
two novel lexical associations for Hindi word sense disambiguation based on the
contextual features of the polysemous word. The evaluation of these methods is
carried out over learning algorithms and favorable results are achieved.
