Document-level Relation Extraction with Cross-sentence Reasoning Graph

• URL: http://arxiv.org/abs/2303.03912v1
• Date: Tue, 7 Mar 2023 14:14:12 GMT
• Title: Document-level Relation Extraction with Cross-sentence Reasoning Graph
• Authors: Hongfei Liu, Zhao Kang, Lizong Zhang, Ling Tian, and Fujun Hua
• Abstract summary: Relation extraction (RE) has recently moved from the sentence-level to document-level. We propose a novel document-level RE model with a GRaph information Aggregation and Cross-sentence Reasoning network (GRACR) Experimental results show GRACR achieves excellent performance on two public datasets of document-level RE.
• Score: 14.106582119686635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Relation extraction (RE) has recently moved from the sentence-level to document-level, which requires aggregating document information and using entities and mentions for reasoning. Existing works put entity nodes and mention nodes with similar representations in a document-level graph, whose complex edges may incur redundant information. Furthermore, existing studies only focus on entity-level reasoning paths without considering global interactions among entities cross-sentence. To these ends, we propose a novel document-level RE model with a GRaph information Aggregation and Cross-sentence Reasoning network (GRACR). Specifically, a simplified document-level graph is constructed to model the semantic information of all mentions and sentences in a document, and an entity-level graph is designed to explore relations of long-distance cross-sentence entity pairs. Experimental results show that GRACR achieves excellent performance on two public datasets of document-level RE. It is especially effective in extracting potential relations of cross-sentence entity pairs. Our code is available at https://github.com/UESTC-LHF/GRACR.

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