Learning rules and aligning elements for document-level relation extraction

doi:10.1016/j.ipm.2025.104511

Learning rules and aligning elements for document-level relation extraction

2025-12-08

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DOI：10.1016/j.ipm.2025.104511

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Document-level relation extraction (DocRE) aims to infer semantic relations between entity pairs1 in a document. Generation-based methods for DocRE only learn superficial text patterns from plain text instead of logical rule patterns while generating uncontrolled outputs. Therefore, this paper proposes a novel generative paradigm, a rule learning and elements alignment (RLEA) method for DocRE. We build a symmetrical structure using two T5 models (text learner and rule learner), where the text learner learns text patterns from symbolic triplets, and the rule learner learns rule patterns from chain-like logic rules. To better solve the above challenges, we proposed three key techniques: the bidirectional gate function, the rule regularizer, and the alignment mechanism. The experimental results indicate that our method achieves state-of-the-art results in relation extraction and logical consistency, with RLEA obtaining 72.37, 79.44 and 94.52 on DWIE w.r.t Ign F1, F1 and Logic respectively, 61.94 and 63.96 on DocRED w.r.t Ign F1 and F1 respectively, 76.81 and 77.06 on Re-DocRED w.r.t Ign F1 and F1 respectively. Besides, quantitative experiments and qualitative analysis show how logical rules work on black-box generation-based models2 for DocRE.

Learning rules and aligning elements for document-level relation extraction

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