基于多通道卷积神经网络的中文文本关系抽取

doi:10.3969/j.issn.1000-5641.2021.03.010

摘要/Abstract

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.

关键词: 关系抽取, 多通道CNN, 注意力机制, 中文文本

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.

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

中图分类号:

TP391

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.

Yanchun LIANG, Ailian FANG. Chinese text relation extraction based on a multi-channel convolutional neural network[J]. Journal of East China Normal University(Natural Science), 2021, 2021(3): 96-104.

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参考文献 19

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序号	关系类别	举例	占比/%
1	Greate(创造)	男人－陶器	2.93
2	Use(使用)	奶奶－蒲扇	4.76
3	Near(邻近)	山－县城	2.76
4	Social(社交关系)	母亲－邻居	6.02
5	Located(位于)	幽兰－山谷	37.43
6	Ownership(拥有)	村民－旧屋	5.10
7	General-Special(一般－特殊)	鱼－鲫鱼	6.99
8	Family(家人)	母亲－奶奶	37.43
9	Part-Whole(部分－整体)	花－仙人掌	23.76

参数	数值	参数	数值
词向量维度	100	z	2
位置向量维度	10	a	2.5
卷积核大小	9	b	0.5
学习速率	0.001	batch	10
卷积核个数	500	epoch	100
隐藏层个数	100	Dropout	0.5

模型	P/%	R/%	F₁/%
BiLSTM	57.18	56.24	56.70
Att-BiLSTM	56.26	59.20	57.70
CNN	49.91	64.74	56.37
MCNN	59.42	59.63	59.52
MCNN_Att	60.72	64.27	62.45
MCNN_Att_P	61.57	65.36	63.41
MCNN_Att_RL	62.87	66.03	64.41

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