Analysis of Statistical Question Classification for Fact-Based Questions

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification 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 effort to create and often suffer from being too specific. Statistical question classification 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 different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.     

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本文是美国圣地亚哥大学教授詹姆斯·A·克莱普2008年4月在北京城市学院的演讲,演讲共三场。为便于读者全面地理解克莱普教授的演讲内容,全部演讲内容被编译成一篇。本文是根据演讲记录稿整理后,经克莱普教授本人审定英文稿编译。