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.