Question Classification plays an important role in most Question Answering systems. In this paper, we exploit semantic features in Support Vector Machines (SVMs) for Question Classification. We propose a semantic tree kernel to incorporate semantic similarity information. A diverse set of semantic features is evaluated. Experimental results show that SVMs with semantic features, especially semantic classes, can significantly outperform the state-of-the-art systems.

Recently, Question Answering has been a hot topic in the research of information retrieval. Question Classification plays a critical role in most Question Answering systems. In this paper, a new approach to classifying questions using Profile Hidden Markov Models (PHMMs) is proposed. The generalization strategies to extract the pattern instances of questions by selective substitution are discussed. Then the classification method with pattern instances' structural features is investigated. Experimental results show that the PHMM based question classifier can reach the accuracy of 92.2% and significantly outperforms most of the state-of-the-art systems.

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