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Multi-granularity Neural Networks for Document-Level Relation Extraction

  • Conference paper
  • First Online:
Web and Big Data (APWeb-WAIM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14965))

  • 175 Accesses

Abstract

Document-level relation extraction aims to extract relations from multiple sentences in a document. However, it remains challenging to obtain rich semantic information across multiple sentences for relation prediction. In this paper, a multi-granularity relation extraction (MGRE) neural network is proposed, which integrates multiple granularity semantic features (i.e., entity level, sentence level and document level), to capture the semantic interactions among entities and sentences in a document. For entities, the shortest dependency path is utilized to obtain head-to-tail entity representations, which is further used for acquiring the relation between entity pairs through a translation strategy. Then, at the sentence level, a convolutional neural network is used to extract semantic features for each sentence. While for the documents, an attention mechanism is adopted to fuse multiple sentence-level feature vectors into document-level semantic features. Finally, entity representation and document representation are combined into a holistic representation for relation prediction. Extensive experiments are conducted on the DocRED dataset against state-of-the-art methods, and the comparative results demonstrate the superiority of MGRE on document-level relation extraction.

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Author information

Authors and Affiliations

  1. Nanjing University of Finance and Economics, Nanjing, 210023, China

    Xiye Chen

  2. Southeast University, Nanjing, 210096, Jiangsu, China

    Peng Wang

Authors
  1. Xiye Chen
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  2. Peng Wang
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Correspondence to Peng Wang .

Editor information

Editors and Affiliations

  1. University of New South Wales, Sydney, NSW, Australia

    Wenjie Zhang

  2. National University of Singapore, Queenstown, Singapore

    Anthony Tung

  3. Zhejiang Normal University, Jinhua, China

    Zhonglong Zheng

  4. University of New South Wales, Sydney, NSW, Australia

    Zhengyi Yang

  5. University of New South Wales, Sydney, NSW, Australia

    Xiaoyang Wang

  6. Zhejiang Normal University, Jinhua, China

    Hongjie Guo

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Chen, X., Wang, P. (2024). Multi-granularity Neural Networks for Document-Level Relation Extraction. In: Zhang, W., Tung, A., Zheng, Z., Yang, Z., Wang, X., Guo, H. (eds) Web and Big Data. APWeb-WAIM 2024. Lecture Notes in Computer Science, vol 14965. Springer, Singapore. https://doi.org/10.1007/978-981-97-7244-5_7

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  • DOI: https://doi.org/10.1007/978-981-97-7244-5_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-7243-8

  • Online ISBN: 978-981-97-7244-5

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