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Three-stage document-level entity relation extraction
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AI
DOI：10.1007/s11227-025-07068-2.png)
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Abstract
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Document-level entity relation extraction aims to identify all potential relations between entity pairs from unstructured text. To address challenges such as multi-mention entities, long-distance entity relations, and complex relation reasoning, this paper proposes a three-stage document relation extraction model (TSDRE) based on text coreference resolution and relation reasoning. Specifically, we first employ the CorefQA model to resolve referential pronouns within the document. Next, the document is segmented into sentences, and the PRGC model is utilized to extract sentence-level relations. Finally, leveraging information on potentially complex relations between entity pairs, relation reasoning, and supporting evidence encodes this information and uses it as input for the BERT model to determine possible relation categories of entity pairs. Experimental results demonstrate that on the DocRED, TSDRE achieves improvements of at least 0.18, 0.15, and 0.21 over other models in terms of validation set F1, test set F1 and lgnF1, respectively. On the DWIE, TSDRE achieves improvements of at least 0.81 and 0.35 for the validation set lgnF1 and the test set lgnF1, respectively. Results indicates that TSDRE excels in accurately and comprehensively extracting entity pair correlation information from documents.
Keywords:
Relation extraction
Documentation
Relational reasoning
Pipeline structure
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