计算机科学

基于多通道卷积神经网络的中文文本关系抽取

  • 梁艳春 ,
  • 房爱莲
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  • 华东师范大学 计算机科学与技术学院,上海 200062

收稿日期: 2020-05-18

  网络出版日期: 2021-05-26

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

  • Yanchun LIANG ,
  • Ailian FANG
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  • School of Computer Science and Technology, East China Normal University, Shanghai 200062, China

Received date: 2020-05-18

  Online published: 2021-05-26

摘要

给出了一种多通道卷积神经网络(Convolutional Neural Network, CNN)方法实现中文文本端到端的关系抽取. 每个通道用分层的网络结构, 在传播过程中互不影响, 使神经网络能学习到不同的表示. 结合中文语言的难点, 加入注意力机制(Attention Mechanism, Att)获取更多的语义特征, 并通过分段平均池化融入句子的结构信息. 经过最大池化层获得句子的最终表示后, 计算关系得分, 并用排序损失函数(Ranking-Loss Function, RL)代替交叉熵函数进行训练. 实验结果表明, 提出的MCNN_Att_RL (Multi CNN_Att_RL)模型能有效提高关系抽取的查准率、召回率和F1值.

本文引用格式

梁艳春 , 房爱莲 . 基于多通道卷积神经网络的中文文本关系抽取[J]. 华东师范大学学报(自然科学版), 2021 , 2021(3) : 96 -104 . DOI: 10.3969/j.issn.1000-5641.2021.03.010

Abstract

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.

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