A new method for updating word senses in Hindi WordNet

Hindi WordNet, a rich computational lexicon is widely being used for many Hindi Natural Language Processing (NLP) applications. However it does not presently provide exhaustive list of senses for every word, which degrades the performance of such NLP applications. In this paper, we propose a graph based model and its associated techniques to automatically acquire words' senses. In the literature no such method is available which is capable of automatically identify the senses of the Hindi words. We use a Hindi part of speech tagged corpus for building the graph model. The linkage between noun-noun concepts is extracted on the basis of syntactic and semantic relationships. All of the senses of a word including the sense which is not present in Hindi WordNet are extracted. Our method also finds the categories of similar words. Using this model applications of NLP can be achieved at a higher level.