Question classification with semantic tree kernel

Authors:
Yan Pan;Yong Tang;Luxin Lin;Yemin Luo
Affiliations:
Sun Yat-sen University, Guangzhou, China;Sun Yat-sen University, Guangzhou, China;Sun Yat-sen University, Guangzhou, China;Sun Yat-sen University, Guangzhou, China
Venue:
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2008

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Abstract

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.