Syntactic Type-aware Graph Attention Network for Drug-drug Interactions and their Adverse Effects Extraction
Peng Chen, Jian Wang, Hongfei Lin, Yichen Wang, Di Zhao, Yijia Zhang · 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2022
Automatic extraction of drug-drug interactions and their adverse effects can promote the research of pharmacovigilance and thus attracts attention from both academia and industry. Recent efforts focus on span-based approaches and show more promising results. However, span-based methods enumerate all possible candidate entity spans while ignoring boundary information of spans. Meanwhile, lacking sufficient interactions in intra-span and inter-span further hinders the performance of the nested entity and overlapping relation extraction. To this end, we propose a syntactic type-aware graph attention network for drug-drug interactions and their adverse effects extraction. Specifically, a boundary heuristic module is designed firstly to generate the boundary of linguistically legitimate entity spans. And then, different from the general syntactic graph (i.e., only considering dependency edges), we construct a syntactic type-aware graph attention network (STG) to capture interactions in intra-span and inter-span by considering syntactic edges and types simultaneously. Results1achieved on two biomedical benchmark datasets, including drug-drug interaction (DDI) and adverse drug effect (ADE), indicate that our model obtains significantly more performance than the state-of-the-art methods, achieving improvements in the relation F1 score of 1.63% on ADE and 2.07% on DDI dataset, respectively.