Advances in question classification for open-domain question answering

Open-domain question answering is becoming a hot topic in the fields of natural language processing and information retrieval. Question classification, as an important component of question answering, has shown its significant influence on the overall performance of question answering systems. It can help reduce the search space and choose the exact search strategy to find answers. In this paper, we present a through overview of the state-of-the-art approaches to question classification, in terms of category/dataset, feature extraction, classification methods and performance metrics. Firstly, we give a detailed analysis of the supervised learning based question classification approaches. Then, we introduce some related work on question classification, such as kernel methods, semi-supervised learning methods, active learning and transfer learning methods, and so on. Finally, we give some possible research directions on question classification. ©, 2015, Chinese Institute of Electronics. All right reserved.