文献类型：期刊文献

英文题名：A co-adaptive duality-aware framework for biomedical relation extraction

作者：Zhang, Weiyan[1];Chen, Chuang[1];Wang, Jiacheng[1];Liu, Jingping[1];Ruan, Tong[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2023

卷号：39

期号：5

外文期刊名：BIOINFORMATICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000995028600002)】；

基金：This work was supported by the National Key Research and Development Program of China [2021YFC2701800, 2021YFC2701801]; the Shanghai Sailing Program [No. 23YF1409400]; and the Zhejiang Lab [No. 2019ND0AB01].

语种：英文

摘要：Motivation: Biomedical relation extraction is a vital task for electronic health record mining and biomedical knowledge base construction. Previous work often adopts pipeline methods or joint methods to extract subject, relation, and object while ignoring the interaction of subject-object entity pair and relation within the triplet structure. However, we observe that entity pair and relation within a triplet are highly related, which motivates us to build a framework to extract triplets that can capture the rich interactions among the elements in a triplet. Results: We propose a novel co-adaptive biomedical relation extraction framework based on a duality-aware mechanism. This framework is designed as a bidirectional extraction structure that fully takes interdependence into account in the duality-aware extraction process of subject-object entity pair and relation. Based on the framework, we design a co-adaptive training strategy and a co-adaptive tuning algorithm as collaborative optimization methods between modules to promote better mining framework performance gain. The experiments on two public datasets show that our method achieves the best F1 among all state-of-the-art baselines and provides strong performance gain on complex scenarios of various overlapping patterns, multiple triplets, and cross-sentence triplets.