Automatically Grouping Questions in Yahoo! Answers

In this paper, we define and study a novel problem which is referred to as Community Question Grouping (CQG). Online QA services such as Yahoo! Answers contain large archives of community questions which are posted by users. Community Question Grouping is primarily concerned with grouping a collection of community questions into predefined categories. We first investigate the effectiveness of two basic methods, i.e., K-means and PLSA, in solving this problem. Then, both methods are extended in different ways to include user information. The experimental results with real datasets show that incorporation of user information improves the basic methods significantly. In addition, performance comparison reveals that PLSA with regularization is the most effective solution to the CQG problem.