FCDS: Fusing Constituency and Dependency Syntax into Document-Level Relation Extraction
This work addresses document-level relation extraction for natural language processing, offering an incremental improvement by integrating additional syntax information.
The paper tackled the problem of document-level relation extraction by fusing constituency and dependency syntax to better exploit rich syntax information, resulting in demonstrated effectiveness across various domain datasets.
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.