Location-enhanced syntactic knowledge for biomedical relation extraction

Biomedical relation extraction has long been considered a challenging task due to the specialization and complexity of biomedical texts. Syntactic knowledge has been widely employed in existing research to enhance relation extraction, providing guidance for the semantic understanding and text representation of models. However, the utilization of syntactic knowledge in most studies is not exhaustive, and there is often a lack of fine-grained noise reduction, leading to confusion in relation classification. In this paper, we propose an attention generator that comprehensively considers both syntactic dependency type information and syntactic position information to distinguish the importance of different dependency connections. Additionally, we integrate positional information, dependency type information, and word representations together to introduce location-enhanced syntactic knowledge for guiding our biomedical relation extraction. Experimental results on three widely used English benchmark datasets in the biomedical domain consistently outperform a range of baseline models, demonstrating that our approach not only makes full use of syntactic knowledge but also effectively reduces the impact of noisy words.