AUTHOR=Yang Jie , Ding Yihao , Long Siqu , Poon Josiah , Han Soyeon Caren 

TITLE=DDI-MuG: Multi-aspect graphs for drug-drug interaction extraction

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 5 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2023.1154133

DOI=10.3389/fdgth.2023.1154133

ISSN=2673-253X

ABSTRACT=Drug-drug interaction (DDI) may lead to adverse reactions in patients, thus it is important to extract such knowledge from biomedical texts. However, previously proposed approaches typically focus on capturing sentence-aspect information while ignoring valuable knowledge concerning the whole corpus. In this paper, we propose a Multi-aspect Graph-based DDI extraction model, named DDI-MuG. We first employ a bio-specific pre-trained language model to obtain the token contextualized representations. Then we use two graphs to get syntactic information from input instance and word co-occurrence information within the entire corpus, respectively. Finally, we combine the representations of drug entities and verb tokens for the final classification. It is encouraging to see that the proposed model outperforms all baseline models on two benchmark datasets. To the best of our knowledge, this is the first model that explores multi-aspect graphs to the DDI extraction task, and we hope it can establish a foundation for more robust multi-aspect works in the future.