Document-level Relation Extraction (DocRE) aims to identify relation labels
between entities within a single document. It requires handling several
sentences and reasoning over them. State-of-the-art DocRE methods use a graph
structure to connect entities across the document to capture dependency syntax
information. However, this is insufficient to fully exploit the rich syntax
information in the document. In this work, we propose to fuse constituency and
dependency syntax into DocRE. It uses constituency syntax to aggregate the
whole sentence information and select the instructive sentences for the pairs
of targets. It exploits the dependency syntax in a graph structure with
constituency syntax enhancement and chooses the path between entity pairs based
on the dependency graph. The experimental results on datasets from various
domains demonstrate the effectiveness of the proposed method. The code is
publicly available at this url.

本研究将成分句法和依赖句法融合到文档级关系抽取（DocRE）中，使用成分句法整合整个句子信息和为目标对选择指导性句子，利用依赖句法在图结构中选择实体对之间的路径。实验结果表明该方法的有效性。