In natural language processing, there exist a problem of determining which "sense" (meaning) of a word is activated by the use of the word in a particular context. Natural language is ambiguous. It is an easy task for a human to understand and disambiguate the ambiguous words but a trivial task for a computer to do so. Ambiguity can occur at various levels of NLP- lexical, syntactic, and semantic and discourse level. The project here concentrates on Lexical Semantic ambiguity task. Lexical Semantic ambiguity takes place when a word/lexicon or a phrase has multiple meanings associated with it. Word Sense Disambiguation (WSD) aims to disambiguate the words which have multiple sense in a context automatically. Sense denotes the meaning of a word and the words which have various meanings in a context are referred as ambiguous words. It is the task of understanding the sense of an ambiguous word in a piece of context. It basically assigns the appropriate sense to a word depending on the particular context. where it occurs in an automated manner. In this report, we propose supervised Machine Learning approach for Word Sense Disambiguation task in English language. Knowledge-based resource like machine readable dictionaries can be used for disambiguation task. We used Natural Language Toolkit(NLTK) to perform various operation on given input given by user. Natural Language Toolkit(NLTK) library, is free, open source tool. This library does the core functioning for our application. It provides training data sets, Wordnet corpus, various tokenizers , lemmatizers and stemmers.

Natural Language Toolkit(NLTK), Ambiguous word, Supervised Machine Learning , Lexical Semantic.

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