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Word Sense Disambiguation (WSD) concerns with selecting an accurate sense of a term automatically in the given situation. It is very significant and challenging problem in several applications of natural language processing. We have used a probabilistic model in our system. Word Sense Disambiguation is done based on n-grams (bigrams and trigrams). The objective is to evaluate the performance of the system with various kinds of an input string which have an ambiguous word. For resolving the probability of the sense of an ambiguous word in the given sentence we use a Naïve Bayes classifier. We have use translation memory (TM) to speed up the translation process. A classical translation memory chooses contender translations into a target language (Hindi) by getting similar translations to an entered text from available pairs of earlier translated segments.

N-gram Based WSD for Improving Accuracy of Machine Translation using TM