Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 5
1324-1329,1336

Task of emotion cause pair extraction based on graph-based dependency parsing

Gao Dechen1
Zhang Benwen2
Zhao Rongmei1
Ju Shenggen1
1. College of Computer Science, Sichuan University, Chengdu 610065, China
2. College of Science & Technology, Sichuan Minzu College, Kangding Sichuan 626000, China
DOI: 10.19734/j.issn.1001-3695.2021.10.0448

Abstract

Emotion cause pair extraction is a subtask in the sentiment analysis task, which aims to extract all emotion clauses in a given document and the cause clauses corresponding to the emotion. The previous work ignores the interrelationship between the emotion clause and the cause clause when generating the expression of the emotion clause and the cause clause. In order to solve the problem, based on the idea of graph-based dependency parsing and incorporating the graph attention mechanism, this paper proposed the GAT-ECPE model. When the model obtained the expression of the emotion clause and the reason clause, it used the sentence vector as a node into the graph attention layer to learn the information about the relationship between the clauses, and then performed biaffine transform to obtain the encoding of the emotion cause pair expression. And it set up a multi-task to establish a relationship between the extraction of emotions and causes. The experimental results on the ECPE data set prove that compared with the previous series of models, this model has improved evaluation indicators.

Foundation Support

国家自然科学基金资助项目(61972270)
四川省新一代人工智能重大专项项目(2018GZDZX0039)
四川省重点研发资助项目(2019YFG0521)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.10.0448
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 5
Section: Algorithm Research & Explore
Pages: 1324-1329,1336
Serial Number: 1001-3695(2022)05-007-1324-06

Publish History

[2021-12-20] Accepted Paper
[2022-05-05] Printed Article

Cite This Article

高德辰, 张本文, 赵容梅, 等. 基于图依存分析的情感原因对抽取任务 [J]. 计算机应用研究, 2022, 39 (5): 1324-1329,1336. (Gao Dechen, Zhang Benwen, Zhao Rongmei, et al. Task of emotion cause pair extraction based on graph-based dependency parsing [J]. Application Research of Computers, 2022, 39 (5): 1324-1329,1336. )

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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