Community Question Topic Categorization via Hierarchical Kernelized Classification

We present a hierarchical kernelized classification model for the automatic classification of general questions into their corresponding topic categories in community Question Answering service (cQAs). This could save many efforts of manual classification and facilitate browsing as well as better retrieving of questions from the cQA archives. To deal with the challenge of short text message of questions, we explore and optimally combine various cQA features by introducing multiple kernel learning strategy into the hierarchical classification framework. We propose a hybrid regularization approach of combining orthogonal constraint and Li sparseness in our framework to promote the discriminative power on similar topics as well as sparsing the model parameters. The experimental results on a real world dataset from Yahoo! Answers demonstrate the effectiveness of our proposed model as compared to the state-of-the-art methods and strong baselines.