Document similarity computation by combining multiple representation models

Evaluating semantic similarity of text document pairs is an active research topic. Various models of document representation have been proposed. Each kind of representation model concentrates on a different kind of information from other kind of models. However, it is difficult for a single model to perform well in all scenarios because of the variety of textual documents. Leveraging these models to complement each other is possible to improve the performance. In this paper, we first make an analysis on the relations among document semantic similarity, human ratings and model performance. Based on the observations, we propose a rational solution of selecting different representation models and fusing the results of these models to compute document similarity for a given document collection. We leverage the performance and relations of different models to select proper models. Our fusion approach proposes a regression function with both nonlinear and linear factors and dynamic weights based on the similarities by various models. We report the effectiveness of our work based on a rated news document collection. The particular version of our general approach for this collection can integrate the information from both brief entity knowledge and detailed word content.