Title
Joint Extraction of Biomedical Entities and Relations based on Decomposition and Recombination Strategy
Publication Name
Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Abstract
Entities and relations extraction is one of the key task to build medical knowledge graph, which is of great significance to the development of medical artificial intelligence. However, overlapping triples are great challenge for biomedical entities and relations extraction. In order to improve the performance of biomedical entities and relations extraction, we propose a joint extraction of entities and relations method based on decomposition and recombination strategy to mine biomedical text. Our method decomposes entities and relations extraction task into three related sub-modules, which are entity tagging module, relation classification module and recombination matching module. Our main contributions are as follows: first, we introduce a decomposition and recombination end-to-end learning framework for joint entities and relations extraction. Second, we propose a bi-directional prediction method to deal with the overlapping triples problem. Finally, we propose the negative samples generation method to alleviate the error accumulation among these modules. The extensive experiments demonstrate that our method can improve the F1 score by 4.36%, 2.13% and 11.72% in ADE, DDI and BB biomedical corpus.
Open Access Status
This publication is not available as open access
First Page
1851
Last Page
1856
Funding Number
62102291
Funding Sponsor
National Natural Science Foundation of China