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A Concise Relation Extraction Method Based on the Fusion of Sequential and Structural Features Using ERNIE

Author

Listed:
  • Yu Wang

    (School of Biomedical Engineering, Anhui Medical University, Hefei 230001, China)

  • Yuan Wang

    (Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230001, China)

  • Zhenwan Peng

    (School of Biomedical Engineering, Anhui Medical University, Hefei 230001, China)

  • Feifan Zhang

    (School of Biomedical Engineering, Anhui Medical University, Hefei 230001, China)

  • Fei Yang

    (School of Biomedical Engineering, Anhui Medical University, Hefei 230001, China)

Abstract

Relation extraction, a fundamental task in natural language processing, aims to extract entity triples from unstructured data. These triples can then be used to build a knowledge graph. Recently, pre-training models that have learned prior semantic and syntactic knowledge, such as BERT and ERNIE, have enhanced the performance of relation extraction tasks. However, previous research has mainly focused on sequential or structural data alone, such as the shortest dependency path, ignoring the fact that fusing sequential and structural features may improve the classification performance. This study proposes a concise approach using the fused features for the relation extraction task. Firstly, for the sequential data, we verify in detail which of the generated representations can effectively improve the performance. Secondly, inspired by the pre-training task of next-sentence prediction, we propose a concise relation extraction approach based on the fusion of sequential and structural features using the pre-training model ERNIE. The experiments were conducted on the SemEval 2010 Task 8 dataset and the results show that the proposed method can improve the F1 value to 0.902.

Suggested Citation

  • Yu Wang & Yuan Wang & Zhenwan Peng & Feifan Zhang & Fei Yang, 2023. "A Concise Relation Extraction Method Based on the Fusion of Sequential and Structural Features Using ERNIE," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1439-:d:1098991
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    References listed on IDEAS

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    1. Alexander Sboev & Roman Rybka & Anton Selivanov & Ivan Moloshnikov & Artem Gryaznov & Alexander Naumov & Sanna Sboeva & Gleb Rylkov & Soyora Zakirova, 2023. "Accuracy Analysis of the End-to-End Extraction of Related Named Entities from Russian Drug Review Texts by Modern Approaches Validated on English Biomedical Corpora," Mathematics, MDPI, vol. 11(2), pages 1-23, January.
    2. Ana Laura Lezama-Sánchez & Mireya Tovar Vidal & José A. Reyes-Ortiz, 2022. "An Approach Based on Semantic Relationship Embeddings for Text Classification," Mathematics, MDPI, vol. 10(21), pages 1-15, November.
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    Cited by:

    1. Ze Shi & Hongyi Li & Di Zhao & Chengwei Pan, 2023. "Research on Relation Classification Tasks Based on Cybersecurity Text," Mathematics, MDPI, vol. 11(12), pages 1-16, June.

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