Abstract:
Automatic extraction of biomedical relations is important for many tasks, such as drug discovery, protein prediction. However, since the biomedical corpus contains a larg...Show MoreMetadata
Abstract:
Automatic extraction of biomedical relations is important for many tasks, such as drug discovery, protein prediction. However, since the biomedical corpus contains a large number of complex long sentences and overlapping triples, most general domain joint modeling methods are unapplicable to the biomedical domain. Aiming at the problem of overlapping triples in the biomedical field, we design a token pair tagger to achieve single-stage joint biomedical entity and relation extraction. In addition, in order to make the tagger better understand the semantic information and structural information, we design both global-aware module and distance-aware module to introduce location information. Compared with previous methods, our model makes better use of location information, enhances the understanding of complex long sentences, and improves the ability to extract overlapping triples. We evaluated our model on the CHEMPROT and DDIExtraction2013 datasets. Experimental results show that our model significantly outperforms a range of baseline models, achieving the state-of-the-art performance.
Date of Conference: 06-08 December 2022
Date Added to IEEE Xplore: 02 January 2023
ISBN Information: