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Enhanced graph convolutional network based on node importance for document-level relation extraction

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Abstract

Document-level relation extraction aims to reason complex semantic relations among entities expressed by multiple associated mentions in a document. Existing methods construct document-level graphs to model interactions between entities. However, these methods only pay attention to the connection relationship of nodes, yet ignore the importance of nodes decided by topological structure. In this paper, we propose a novel method, named Enhanced Graph Convolutional Network (EGCN), to extract document-level relations. Unlike previous methods that only model the connection relationship between two nodes, we further exploit the global topological structural information by measuring node importance. We merge these non-local relationship into a Graph Convolutional Network to aggregate relevant information. In addition, to model semantic and syntactic interactions in documents, we design a novel strategy to construct document-level heterogeneous graphs with different types of edges. Experimental results demonstrate that our EGCN outperforms the previous models by 5.54%, 1.7%, and 2.9% \(F_1\) on three public document-level relation extraction datasets.

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Acknowledgements

We thank all editors and reviewers for their effort and help to improve this work. This work is supported by the Postgraduate Research Innovation Program of Jiangsu Province (No. KYCX21_0328).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Qi Sun, Kun Zhang, Kun Huang, Xun Li & Ting Zhang

  2. Key Laboratory of Acupuncture and Medicine Research of Ministry of Education, Nanjing University of Chinese Medicine, Nanjing, 210023, China

    Tiancheng Xu

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  1. Qi Sun
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  2. Kun Zhang
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  3. Kun Huang
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Correspondence to Kun Zhang.

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Sun, Q., Zhang, K., Huang, K. et al. Enhanced graph convolutional network based on node importance for document-level relation extraction. Neural Comput & Applic 34, 15429–15439 (2022). https://doi.org/10.1007/s00521-022-07223-3

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