SUPERVISED WORD SENSE DISAMBIGUATION BASED ON VECTOR SPACE MODEL

Word sense disambiguation（WSD） is the key problem in natural language processing because the result of WSD affects seriously many problems in natural language processing and information retrieval. Because of the failure of manpower on WSD, many supervised methods in machine learning were used on this problem. In this paper, a supervised method is proposed to formalize the senses of polysemous word with interesting term weight based on vector space model, then to deal with WSD with k-NN（k=1）. The experiments on 9 Chinese polysemous words in both open test and close test with average accuracy 96.31% in close test and 92.98% in open test show that the method in this paper is very good.