Dual Attention Guided Graph Convolutional Networks for Relation Extraction

被引：0
|
作者：
Li Z.-X. [1 ]
Sun Y.-R. [1 ]
Tang S.-Q. [1 ]
Zhang C.-L. [1 ]
Ma H.-F. [2 ]
机构：
[1] Guangxi Key Laboratory of Multi-source Information Mining and Security, Guangxi Normal University, Guilin
[2] College of Computer Science and Engineering, Northwest Normal University, Lanzhou
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2021年 / 49卷 / 02期
关键词：
Attention mechanism; Graph convolutional network; Multi-hop relational reasoning; Relation extraction;
D O I：
10.12236/DZXB.20191105
中图分类号：
学科分类号：
摘要：
To better learn node dependence and make use of structural information, this paper proposes a new method that takes the tree of complete dependence as the direct input.The method uses the graph convolutional network and combines two parallel attention modules to learn how to select the useful information.The method represents the samples as nodes on the graph.One module is used to compute the influence between positions of node features, which allows the feature vector to contain a wider range of semantic information.The other one is used to compute the relational features of node dependence, so as to enhance the global dependence between nodes.The two modules promote each other in parallel to obtain complete feature representation.The experimental results on the TACRED and SemEval datasets show that the method can obtain more useful information for relation extraction, thus achieve better performances on various evaluation metrics. © 2021, Chinese Institute of Electronics. All right reserved.
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页码：315 / 323
页数：8
相关论文
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