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Document-level relation extraction with structural encoding and entity-pair-level information interaction
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AI
DOI：10.1016/j.eswa.2024.126099.png)
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Abstract
En 中文
Document-level relation extraction focuses on identifying the relations between entity pairs across the entirety of a document. However, current mainstream methods mainly have two drawbacks: (a) Separating the context encoding stage and the graph reasoning stage. (b) Limiting the reasoning between entity mentions, which ignores the relation correlations in context. In this paper, we introduce the SE-REPI model, which consists of a Structural-enhanced Encoding (SE) module and a Relation-based Entity-Pair-Level Interaction (REPI) module. The SE module effectively injects structural information into the architecture of the Transformer, integrating the graph reasoning stage into the encoding stage. The REPI module constructs entity-pair-level information interaction, which effectively captures the relation correlations among entity pairs. Comprehensive experimental results on four public DocRE datasets DocRED, CRD, GDA, and DWIE underscore the superior performance and efficiency of our model compared to previous leading-edge methods. Further experimental analyses reveal the interpretability of our approach.
Keywords:
Relation extraction
Structural transformer
Information extraction
Natural Language Processing
Journal
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7.5
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3.0W
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10.2W
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