Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (2): 350-356.DOI: 10.11772/j.issn.1001-9081.2020081310

Special Issue: 人工智能

• Artificial intelligence • Previous Articles     Next Articles

Relation extraction model via attention-based graph convolutional network

WANG Xiaoxia, QIAN Xuezhong, SONG Wei   

  1. School of Artifical Intelligence and Computer Science, Jiangnan University, Wuxi Jiangsu 214122, China
  • Received:2020-08-27 Revised:2020-12-10 Online:2021-02-10 Published:2020-12-17
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61673193), the China Postdoctoral Science Foundation (2017M621625), the Natural Science Foundation of Jiangsu Province (BK20181341).

基于注意力与图卷积网络的关系抽取模型

王晓霞, 钱雪忠, 宋威   

  1. 江南大学 人工智能与计算机学院, 江苏 无锡 214122
  • 通讯作者: 王晓霞
  • 作者简介:王晓霞(1995-),女,山东滨州人,硕士研究生,主要研究方向:自然语言处理、关系抽取;钱雪忠(1967-),男,江苏无锡人,副教授,硕士,CCF会员,主要研究方向:数据挖掘、人工智能;宋威(1981-),男,湖北恩施人,教授,博士,CCF会员,主要研究方向:数据挖掘、人工智能。
  • 基金资助:
    国家自然科学基金资助项目(61673193);中国博士后科学基金资助项目(2017M621625);江苏省自然科学基金资助项目(BK20181341)。

Abstract: Aiming at the problem of low information utilization rate of sentence dependency tree and poor feature extraction effect in relation extraction task, an Attention-guided Gate perceptual Graph Convolutional Network (Att-Gate-GCN) model was proposed. Firstly, a soft pruning strategy based on the attention mechanism was used to assign weights to the edges in the dependency tree through the attention mechanism, thus mining the effective information in the dependency tree and filtering the useless information at the same time. Secondly, a gate perceptual Graph Convolutional Network (GCN) structure was constructed, thus increasing the feature perception ability through the gating mechanism to obtain more robust relationship features, and combining the local and non-local dependency features in the dependency tree to further extract key information. Finally, the key information was input into the classifier, then the relationship category label was got. Experimental results indicate that, compared with the original graph convolutional network relation extraction model, the proposed model has the F1 score increased by 2.2 percentage points and 3.8 percentage points on SemEval2010-Task8 dataset and KBP37 dataset respectively, which makes full use of effective information, and improves the relation extraction ability of the model.

Key words: relation extraction, dependency tree, attention mechanism, Graph Convolutional Network (GCN), gate mechanism

摘要: 针对关系抽取任务中句子依存树的信息利用率低和特征提取效果不佳的问题,提出了一种基于注意力引导的门控感知图卷积网络(Att-Gate-GCN)模型。首先,利用一种基于注意力机制的软剪枝策略,通过注意力机制为依存树中的边分配权重,以挖掘依存树中的有效信息,同时过滤无用信息;其次,构建一种门控感知图卷积网络(GCN)结构,通过门控机制增加特征感知能力,以获取更鲁棒的关系特征,同时结合依存树中的局部与非局部依赖特征,进一步抽取关键信息;最后,将关键信息输入分类器得到关系类别标签。实验结果表明,相较于原始的图卷积网络关系抽取模型,所提模型在SemEval2010-Task8数据集和KBP37数据集上F1值分别有2.2个百分点和3.8个百分点的提升,能够更充分地利用有效信息,提升了模型的关系抽取能力。

关键词: 关系抽取, 依存树, 注意力机制, 图卷积网络, 门控机制

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