Joint Biomedical Entity and Relation Extraction Based on Triple Region Vertices

Automatic extraction of biomedical entities and their relations plays a significant role in biomedical curation tasks. Currently, the table-filling methods have received lots of attention in the general domain. However, the presence of complex lengthy sentences and overlapping relations in biomedical texts makes automatic extraction a challenging task. To address this challenge, we propose a joint extraction table-filling method based on the vertices of the triple region. We extract triples by using multi-label classification to mark the boundaries of the triples, fully utilizing the boundary information of the entities. To incorporate the information of the distance between entity pairs, distance embedding is introduced and dilated convolutions are utilized to capture multi-scale contextual information. We evaluated our model on the CHEMPROT and DDIExtraction2013 datasets. The experimental results demonstrate that our model achieves the state-of-the-art performance on both datasets.