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A Relation-Oriented Approach for Complex Entity Relation Extraction

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1966))

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Abstract

Entity relation extraction targets the extraction of structured triples from unstructured text, and is the start of the entire knowledge graph lifecycle. In recent advances, Machine Reading Comprehension (MRC) based approaches provide new paradigms for entity relationship extraction and achieve the state-of-the-art performance. Aiming at the features of nested entities, overlapping relationships and distant entities in recipe texts, this study proposes a relation-oriented approach for complex entity relation extraction. This approach addresses the entity redundancy problem caused by the traditional pipeline models which are entity-first methods. By predicting the starting and ending of entities, it solves the problem of nested entities that cannot be identified by traditional sequence labeling methods. Finally, the error propagation issue is mitigated by the triple determination module. We conduct extensive experiments on multi-datasets in both English and Chinese, and the experimental results show that our method significantly outperforms the baseline model in terms of both precision, recall and micro-F1 score.

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Author information

Authors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Xinliang Liu & Mengqi Zhang

  2. Key Laboratory of Agricultural Informatization Standardization, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China

    Xinliang Liu

  3. National Engineering Research Center for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing, 100048, China

    Xinliang Liu

Authors
  1. Xinliang Liu
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  2. Mengqi Zhang
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Corresponding author

Correspondence to Mengqi Zhang .

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Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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Liu, X., Zhang, M. (2024). A Relation-Oriented Approach for Complex Entity Relation Extraction. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1966. Springer, Singapore. https://doi.org/10.1007/978-981-99-8148-9_38

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  • DOI: https://doi.org/10.1007/978-981-99-8148-9_38

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8147-2

  • Online ISBN: 978-981-99-8148-9

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