No abstract

Document similarity computation is an exciting research topic in information retrieval (IR) and it is a key issue for automatic document categorization, clustering analysis, fuzzy query and question answering. Topic model is an emerging field in natural language processing (NLP), IR and machine learning (ML). In this paper, we apply a latent Dirichlet allocation (LDA) topic model-based method to compute similarity between documents. By mapping a document with term space representation into a topic space, a distribution over topics derived for computing document similarity. An empirical study using real data set demonstrates the efficiency of our method.