Question classication systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classication is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious eort to create and often suer from being too specic. Statistical question classication methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role dierent syntactic and semantic features have on performance. We nd that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassication error and provide insight into ways they may be overcome
