Enhancing Document Clustering Using Hybrid Models for Semantic Similarity

Different document representation models have been proposed to measure semantic similarity between documents using corpus statistics. Some of these models explicitly estimate semantic similarity based on measures of correlations between terms, while others apply dimension reduction techniques to obtain latent representation of concepts. This paper proposes new hybrid models that combine explicit and latent analysis to estimate semantic similarity between documents. The proposed models have been used to enhance the performance of document clustering algorithms. Experiments on thirteen benchmark data sets show that hybrid models achieve significant improvement in clustering performance when used with clustering algorithms that are sensitive to errors in estimating document similarity.