Paper: Question Classification using Head Words and their Hypernyms

ACL ID D08-1097
Title Question Classification using Head Words and their Hypernyms
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

Question classification plays an important role in question answering. Features are the key to obtain an accurate question classifier. In con- trast to Li and Roth (2002)’s approach which makes use of very rich feature space, we pro- pose a compact yet effective feature set. In particular, we propose head word feature and present two approaches to augment semantic features of such head words using WordNet. In addition, Lesk’s word sense disambigua- tion (WSD) algorithm is adapted and the depth of hypernym feature is optimized. With fur- ther augment of other standard features such as unigrams, our linear SVM and Maximum Entropy (ME) models reach the accuracy of 89.2% and 89.0% respectively over a standard benchmark dataset, which outperform the best previously reported accuracy of 86.2...