Word Sense Disambiguation (WSD) is a challenging problem of Natural Language Processing (NLP). Though there are lots of algorithms for WSD available, still little work is carried out for choosing optimal algorithm for that. The job of word sense disambiguation is to decide the accurate meaning of an ambiguous term in a particular circumstance. When WSD is used in machine translation, an accurate translation in the resultant linguistic must be determined for an ambiguous term entry in the original language. Therefore Word Sense Disambiguation remains one of the most common real life problems that are associated to natural language processing which needs to be resolved efficiently.Unsupervised techniques use online dictionary for learning, and supervised techniques use manual learning sets. As there are some advantages and disadvantages of supervised learning and unsupervised learning, aim of this paper is to disambiguate the ambiguous word by using the hybrid approach for WSD. We have made use of parallel corpus and aligned the text by using GIZA.

WSD, Machine Translation, Corpus, Supervised, Unsupervised

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