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CLGR-Net: a collaborative local-global reasoning network for document-level relation extraction

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Abstract

Document-level relation extraction aims to model the reasoning information over multiple sentences of a document and capture complex dependency interactions between inter-sentence entities. However, modeling reasoning information effectively in the document remains a challenging task. In this paper, we propose a Collaborative Local-Global Reasoning Network (CLGR-Net) for the Document-Level Relation Extraction model to effectively predict such relations by integrating rich local and global information from the multi-granularity graph. Specifically, CLGR-Net first constructs a mention-level graph and a concept-level graph. The former aggregates complex local interactions underlying the same entities, the latter captures long-distance global interaction among different entities. Finally, it creates an entity-level graph, the nodes and edges of the entity graph are aggregated by Relational Graph Convolutional Networks (R-GCN) and enriched by probability Knowledge Graphs (KGs), based on which we design a novel hybrid reasoning mechanism to collaborate relevant global and local information for entities. In this way, our model can effectively model reasoning information from these three graphs. The mention-level graph and concept-level graph are used as auxiliary information for the entity-level graph in the form of independent heterogeneous graphs. Our CLGR-Net model achieves more competitive performance than state-of-the-art on three widely used benchmarks.

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Data Availability

The datasets used in the experiments are publicly available in the online repository.

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Acknowledgements

This work is supported by the Natural Science Foundation of Henan Province, China, under grant No. 222300420590. Also, we thank all the reviewers and editors for their feedback.

Funding

This study was financed in part by the Natural Science Foundation of Henan Province, China under grant No. 222300420590.

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Authors and Affiliations

  1. PLA Strategic Support Force Information Engineering University, Zhengzhou, 450000, China

    Xiaoyao Ding, Gang Zhou, Jicang Lu & Taojie Zhu

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  1. Xiaoyao Ding
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  2. Gang Zhou
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Contributions

XD Conceptualization, Design, Software, Validation, Formal analysis, Investigation, Data curation, Writing—original draft, Writing—review & editing, and Visualization. GZ Conceptualization, Writing—review & editing, and Supervision. JL Conceptualization, Writing—review & editing, and Supervision. TZ Conceptualization, Writing—review & editing, and Supervision.

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Correspondence to Gang Zhou.

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Ding, X., Zhou, G., Lu, J. et al. CLGR-Net: a collaborative local-global reasoning network for document-level relation extraction. J Supercomput 79, 5469–5485 (2023). https://doi.org/10.1007/s11227-022-04875-9

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