Improving Word Sense Induction by Exploiting Semantic Relevance

Word Sense Induction (WSI) is the task of automatically inducing the different senses of a target word from unannotated text. Traditional approaches based on the vector space model (VSM) represent each context of a target word as a vector of selected features (e.g. the words occurring in the context). These approaches assume that the words occurring in the context are independent and do not exploit semantic relevance between words. In this paper we propose a WSI method which can exploit semantic relevance between words by incorporating a word graph into the framework of clustering of context vectors. The method is evaluated on the testing data of the Chinese Word Sense Induction task of the first CIPSSIGHAN Joint Conference on Chinese Language Processing (CLP2010). Experimental results show that our method significantly outperforms the baseline methods.