Document-level Biomedical Relation Extraction Based on Relation-guided Entity-level Graphs

Liangyu Gao, Zhihao Yang, Haixin Tan, Lei Wang, Wei Liu, Yin Zhang, Ling Luo, Bo Xu, Jian Wang, Yumeng Yang, Zhehuan Zhao, Yuanyuan Sun, Hongfei Lin

Abstract: The task of document-level biomedical relation extraction involves identifying relational facts between entities across sentences, given specific entities. However, most current methods overlook the associations between entity pairs and generate fixed entity representations merely through mentions, leading to irrelevant mentions interfering with the determination of relational facts. Additionally, these methods fail to consider the global information and dependencies between relational entities. To address these issues, we propose a document-level relation extraction model based on relation-guided entity-level graphs. Our model aggregates all mentions of the same entity through a relation-guided attention mechanism to obtain flexible entity representations. Furthermore, by using U-Net to generate entity-level feature graphs, it facilitates global interactions and dependency capture between entity pairs. Experimental results on two benchmark datasets demonstrate the advantages of our approach in document-level biomedical relation extraction.