Automatic Word Sense Clustering Using Collocation for Sense Adaptation

A specific sense of a word can be determined by collocation of the words gathered from the large corpus that includes context patterns. However, homonym collocation often causes semantic ambiguity. Therefore, the results extracted from corpus should be classified according to every meaning of a word in order to ensure correct collocation. In this paper, K-means clustering is used to solve this problem. This paper reports collocation conditions as well as normalized algorithms actually adopted to address this problem. As a result of applying the proposed method to selected homonyms, the optimal number of semantic clusters showed similarity to those in the dictionary. This approach can disambiguate the sense of homonyms optimally using extracted texts, thus resolving the ambiguity of homonyms arising from collocation.