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 this https URL.

本文提出了一种新颖的文档级关系抽取模型 GRACR，其中构建了一个简化的语义图来建模文档中的所有实体和句子信息，以及一个实体级图来探索长距离跨句子实体对的关系。经实验证明，在文档级别关系抽取任务上，该方法取得了优秀的性能，并且在提取跨句子实体对的潜在关系方面尤其有效。