Using Dependency Analysis to Improve Question Classification

Question classification is a first necessary task of automatic question answering systems. Linguistic features play an important role in developing an accurate question classifier. This paper proposes to use typed dependencies which are extracted automatically from dependency parses of questions to improve accuracy of classification. Experiment results show that with only surface typed dependencies, one can improve the accuracy of a discriminative question classifier by over 8.0% on two benchmark datasets.