research-article

Dependency Parsing-based Entity Relation Extraction over Chinese Complex Text

Authors:

Feiyu ShangAuthors Info & Claims

Transactions on Asian and Low-Resource Language Information Processing, Volume 20, Issue 4

Article No.: 67, Pages 1 - 34

https://doi.org/10.1145/3450273

Published: 09 June 2021 Publication History

Abstract

Open Relation Extraction (ORE) plays a significant role in the field of Information Extraction. It breaks the limitation that traditional relation extraction must pre-define relational types in the annotated corpus and specific domains restrictions, to realize the goal of extracting entities and the relation between entities in the open domain. However, with the increase of sentence complexity, the precision and recall of Entity Relation Extraction will be significantly reduced. To solve this problem, we present an unsupervised Clause_CORE method based on Chinese grammar and dependency parsing features. Clause_CORE is used for complex sentences processing, including decomposing complex sentence and dynamically complementing sentence components, which can reduce sentences complexity and maintain the integrity of sentences at the same time. Then, we perform dependency parsing for complete sentences and implement open entity relation extraction based on the model constructed by Chinese grammar rules. The experimental results show that the performance of Clause_CORE method is better than that of other advanced Chinese ORE systems on Wikipedia and Sina news datasets, which proves the correctness and effectiveness of the method. The results on mixed datasets of news data and encyclopedia data prove the generalization and portability of the method.

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Index Terms

Dependency Parsing-based Entity Relation Extraction over Chinese Complex Text
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 20, Issue 4

July 2021

419 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3465463

Editor:
Imed Zitouni
Google, USA

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Copyright © 2020 Association for Computing Machinery.

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Publication History

Published: 09 June 2021

Accepted: 01 February 2021

Revised: 01 January 2021

Received: 01 September 2019

Published in TALLIP Volume 20, Issue 4

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Cited By

Ma TDu XWu XZhou ZZheng YJin C(2023)Reading Scene Text with Aggregated Temporal Convolutional EncoderACM Transactions on Asian and Low-Resource Language Information Processing10.1145/362582222:11(1-16)Online publication date: 12-Oct-2023
https://dl.acm.org/doi/10.1145/3625822
Abdullah MAziz NAbdulkadir SAlhussian HTalpur N(2023)Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and ChallengesIEEE Access10.1109/ACCESS.2023.324089811(10535-10562)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3240898
Zhang CWang ZZhou GChang FMa DJing YCheng WDing KZhao D(2023)Towards new-generation human-centric smart manufacturing in Industry 5.0Advanced Engineering Informatics10.1016/j.aei.2023.10212157:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.aei.2023.102121
Xue YZhu JLyu J(2022)Construction and Application of Text Entity Relation Joint Extraction Model Based on Multi-Head Attention Neural NetworkComputational Intelligence and Neuroscience10.1155/2022/15302952022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1530295
Zheng QYong YLin JXu FFu HSun Z(2022)Research on the discrepancy distinguishment of the electric power standard clauses based on NLP2022 4th International Academic Exchange Conference on Science and Technology Innovation (IAECST)10.1109/IAECST57965.2022.10062189(146-149)Online publication date: 9-Dec-2022
https://doi.org/10.1109/IAECST57965.2022.10062189

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