Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 2
424-431

Joint entity relation extraction based on graph neural network

Miao Lin
Zhang Yingjun
Xie Binhong
Li Yu
College of Computer Science & Technology, Taiyuan University of Science & Technology, Taiyuan 030024, China
DOI: 10.19734/j.issn.1001-3695.2021.07.0318

Abstract

Joint extraction of entities and relations from unstructured text is an important task in information extraction. The existing methods have achieved considerable performance, but are still subject to some inherent limitations, such as error propagation, redundancy of relation prediction, inability to solve the problem of relations overlap, etc. For this reason, this paper proposed a joint entity relationship extraction model BSGB(BiLSTM+SDA-GAT+BiGCN) based on graph neural network. BSGB was a two-stage predicting process. The first stage of this model extended the semantic dependency analysis to the semantic dependency graph, and proposed a graph attention network to integrate the semantic dependency graph(SDA-GAT). By stacking BiLSTM and SDA-GAT, it extracted sentence sequence and local dependent features, and performed entity span detection and preliminary relationship prediction. In the second stage, it constructed the relation-weighted GCN, which further modeled the interaction between entities and relations, and completed the final extraction of entity-relationship triples. The experimental results on the NYT dataset show that the F1 value of this model reaches 67.1%, which is 5.2% higher than the baseline model in this dataset, and the prediction of the overlap relation is also significantly improved.

Foundation Support

山西省重点研发计划资助项目(201703D111027,201803D121048,201803D121055)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.07.0318
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 2
Section: Algorithm Research & Explore
Pages: 424-431
Serial Number: 1001-3695(2022)02-017-0424-08

Publish History

[2021-11-13] Accepted Paper
[2022-02-05] Printed Article

Cite This Article

苗琳, 张英俊, 谢斌红, 等. 基于图神经网络的联合实体关系抽取 [J]. 计算机应用研究, 2022, 39 (2): 424-431. (Miao Lin, Zhang Yingjun, Xie Binhong, et al. Joint entity relation extraction based on graph neural network [J]. Application Research of Computers, 2022, 39 (2): 424-431. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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