Chinese question classification based on semantic joint features

Abstract

Question classification is an important research content in automatic question-answering system. Chinese question sentences are different from long texts and those short texts like comments on product. They generally contain interrogative words such as who, which, where or how to specify the information required, and include complete grammatical components in the sentence. Based on these characteristics, we propose a more effective feature extraction method for Chinese question classification in this paper. We first extract the head verb of the sentence and its dependency words combined with interrogative words of the sentence as our base features. And then we use latent semantic analysis to help remove semantic noises from the base features. In the end, we expand those features to be semantic representation features by our weighted word-embedding method. Several experimental results show that our semantic joint feature extraction method outperforms classical syntactic based or content vector based method and superior to convolutional neural network based sentence classification method.