Question Fine-grained Classification Based on Semantic Expansion and Attention Network

Abstract

For the fine-grained classification of question texts,which include that the features of text are sparse,the overall features of the text are similar,and the features of local differences are difficult to extract,a classification method based on the combination of semantic expansion and attention network is proposed.The semantic unit is extracted by the dependency syntax analysis tree,and the similar semantic regions around the semantic unit are calculated and expanded in the vector space model.The Long Short Term Memory(LSTM) network model is used to encode the extended text,the attention mechanism is introduced to generate the vector representation of the question text,and the problem text is classified according to the Softmax classifier.Experimental results show that compared with the traditional text classification method based on deep learning network,this method can extract more important classification features and has better classification effect.