Document Clustering with Semantic Analysis

Document clustering generates clusters from the whole document collection automatically and is used in many fields, including data mining and information retrieval. In the traditional vector space model, the unique words occurring in the document set are used as the features. But because of the synonym problem and the polysemous problem, such a bag of original words cannot represent the content of a document precisely. In this paper, we investigate using the sense disambiguation method to identify the sense of words to construct the feature vector for document representation. Our experimental results demonstrate that in most conditions, using sense can improve the performance of our document clustering system. But the comprehensive statistical analysis performed indicates that the differences between using original single words and using senses of words are not statistically significant. In this paper, we also provide an evaluation of several basic clustering algorithms for algorithm selection.