Journal of East China Normal University(Natural Science) ›› 2021, Vol. 2021 ›› Issue (3): 96-104.doi: 10.3969/j.issn.1000-5641.2021.03.010

• Computer Science • Previous Articles     Next Articles

Chinese text relation extraction based on a multi-channel convolutional neural network

Yanchun LIANG, Ailian FANG*()   

  1. School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
  • Received:2020-05-18 Online:2021-05-25 Published:2021-05-26
  • Contact: Ailian FANG


This paper presents an end-to-end method for Chinese text relation extraction based on a multi-channel CNN (convolutional neural network). Each channel is stacked with a layered neural network; these channels do not interact during recurrent propagation, which enables a neural network to learn different representations. Considering the nuances of the Chinese language, we employed the attention mechanism to extract the semantic features of a sentence, and then integrate structural information using piecewise average pooling. After the maximum pooling layer, the final representation of the sentence is obtained and a relational score is calculated. Finally, the ranking-loss function is used to replace the cross-entropy function for training. The experimental results show that the MCNN_Att_RL (Multi CNN_Att_RL) model proposed in this paper can effectively improve the precision, recall, and F1 value of entity relation extraction.

Key words: relation extraction, multi-channel CNN, attention mechanism, Chinese text

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