Document-level relation extraction (DocRE) is a task that focuses on
identifying relations between entities within a document. However, existing
DocRE models often overlook the correlation between relations and lack a
quantitative analysis of relation correlations. To address this limitation and
effectively capture relation correlations in DocRE, we propose a relation graph
method, which aims to explicitly exploit the interdependency among relations.
Firstly, we construct a relation graph that models relation correlations using
statistical co-occurrence information derived from prior relation knowledge.
Secondly, we employ a re-weighting scheme to create an effective relation
correlation matrix to guide the propagation of relation information.
Furthermore, we leverage graph attention networks to aggregate relation
embeddings. Importantly, our method can be seamlessly integrated as a
plug-and-play module into existing models. Experimental results demonstrate
that our approach can enhance the performance of multi-relation extraction,
highlighting the effectiveness of considering relation correlations in DocRE.

我们提出了一种关系图方法，用于明确利用关系之间的相互依赖性，在文档级别关系抽取中考虑关系的相互关联，并通过关系图模型化关系之间的相关性，以及通过关系信息传播的关系相关矩阵的重要性。实验结果验证了我们的方法可以提高多关系抽取的性能，在文档级别关系抽取中考虑关系的相互关联的有效性。