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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 9
2685-2689

Chinese relation extraction pipeline model based on entity cascading types

Rao Dongning
Wu Qianmei
Huang Guanju
School of Computers, Guangdong University of Technology, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2023.12.0621

Abstract

End-to-end entity relation extraction can be decomposed into named entity recognition and relation extraction, most recent works model these two subtasks jointly. Existing pipelined approaches validate the importance of fusing entity type information in the relation model and the potential of pipeline models, but they ignore the possibility that certain entities in the text may have multiple types at the same time, which is particularly common in Chinese datasets. This paper proposed an entity cascading type mechanism to address the aforementioned issues and developed a pipeline model named CENTRELINE, which was more suitable for Chinese relation extraction. This pipelined approach incorporated an entity module, which was a word-word relation classification model. It employed BERT and bi-directional LSTM as encoders, introduced dilated convolution after conditional layer normalization, and finally generated outputs for entities and their cascading types using a cascading type predictor. The input of the relation module was only constructed by the entity module. The F1 values of this method surpass the baseline by 7.23%, 6.93%, and 8.51% on DuIE1.0, DuIE2.0, and CMeIE-V2 datasets, respectively. This method achieves state-of-the-art performance on both DuIE1.0 and DuIE2.0 datasets. The results of ablation experiments indicate that both the proposed cascading type mechanism and the pipeline model refined based on Chinese language characteristics can enhance the performance of relation extraction.

Foundation Support

广东省自然科学基金面上项目(2021A1515012556)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.12.0621
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 9
Section: Algorithm Research & Explore
Pages: 2685-2689
Serial Number: 1001-3695(2024)09-017-2685-05

Publish History

[2024-03-19] Accepted Paper
[2024-09-05] Printed Article

Cite This Article

饶东宁, 吴倩梅, 黄观琚. 基于实体级联类型的中文关系抽取管道模型 [J]. 计算机应用研究, 2024, 41 (9): 2685-2689. (Rao Dongning, Wu Qianmei, Huang Guanju. Chinese relation extraction pipeline model based on entity cascading types [J]. Application Research of Computers, 2024, 41 (9): 2685-2689. )

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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