• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

Computer Engineering & Science ›› 2021, Vol. 43 ›› Issue (06): 1076-1080.

Previous Articles     Next Articles

A question similarity calculation method based on RCNN

YANG De-zhi,KE Xian-xin,YU Qi-chao,YANG Bang-hua   

  1. (School of Mechatronic Engineering and Automation,Shanghai University,Shanghai 200444,China)

  • Received:2019-11-15 Revised:2020-06-16 Accepted:2021-06-25 Online:2021-06-25 Published:2021-06-22

Abstract: Using deep learning to calculate the question similarity in search engine and question answering system is a hotspot in the NLP field. Combining convolutional neural network (CNN) and long-short memory network (LSTM), a recursive convolutional neural network (RCNN) question similarity calculation method is proposed. Firstly, the bidirectional recursive neural network is utilized to extract context information, and then the 1D Convolutional Neural Network was used to integrate the word embedded information with the context information. Then the global maximum pooling is used to extract the key information to complete the semantic representation of the two questions.Finally, the similarity of the question pair is judged through the matching layer. The experimental results show that, based on the Quora Question Pairs data set, the accuracy of the question similarity calculation method is 83.57%, which is better than other methods.


Key words: question similarity, recursive convolutional neural network, global maximum pooling, siamese network