Document-Level Relation Extraction with Relation Correlation Enhancement
Yusheng Huang Zhouhan Lin
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.