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.

翻译：关系抽取（RE）近来已从句子级发展到文档级，这需要聚合文档信息并利用实体和提及进行推理。现有工作将具有相似表示的实体节点和提及节点置于文档级图中，其复杂的边可能引入冗余信息。此外，现有研究仅关注实体级推理路径，而未考虑跨句实体间的全局交互。为此，我们提出了一种新颖的文档级RE模型——图信息聚合与跨句推理网络（GRACR）。具体而言，我们构建了简化文档级图以建模文档中所有提及和句子的语义信息，并设计了实体级图以探索长距离跨句实体对的关系。实验结果表明，GRACR在两个公开的文档级RE数据集上取得了优异性能，尤其在提取跨句实体对的潜在关系方面效果显著。我们的代码开源在https://github.com/UESTC-LHF/GRACR。