Query Classification Based on Regularized Correlated Topic Model

This paper addresses the problem of query classification (QC), which aims to classify Web search queries into one or more predefined categories. The state-of-the-art solution for QC is to employ a bridging classifier via an intermediate taxonomy. In this paper, we advanced the bridging method by leveraging probabilistic topic models. The topic model, referred as RCTM (Regularized Correlated Topic Model), is an extension of the conventional CTM (Correlated Topic Model). RCTM learns a topic model by leveraging weak supervision from existing annotated data rather than in an unsupervised fashion, and thus it can effectively address the problem in topic modeling while the topics are predefined. The experimental evaluations show that our QC approach outperforms other baseline methods.