Investigation of question classifier in question answering

Abstract

In this paper, we investigate how an accurate question classifier contributes to a question answering system. We first present a Maximum Entropy (ME) based question classifier which makes use of head word features and their WordNet hypernyms. We show that our question classifier can achieve the state of the art performance in the standard UIUC question dataset. We then investigate quantitatively the contribution of this question classifier to a feature driven question answering system. With our accurate question classifier and some standard question answer features, our question answering system performs close to the state of the art using TREC corpus. © 2009 ACL and AFNLP.