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        <title>DDI-MuG: Multi-aspect Graphs for Drug-Drug Interaction Extraction</title>
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    <abstract>Drug-drug interaction (DDI) may leads 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.</abstract>
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%0 Conference Proceedings
%T DDI-MuG: Multi-aspect Graphs for Drug-Drug Interaction Extraction
%A Yang, Jie
%A Ding, Yihao
%A Long, Siqu
%A Poon, Josiah
%A Han, Soyeon Caren
%S Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F yang-etal-2022-ddi
%X Drug-drug interaction (DDI) may leads 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.
%U https://aclanthology.org/2022.louhi-1.15
%P 127-137

Markdown (Informal)

[DDI-MuG: Multi-aspect Graphs for Drug-Drug Interaction Extraction](https://aclanthology.org/2022.louhi-1.15) (Yang et al., Louhi 2022)

• DDI-MuG: Multi-aspect Graphs for Drug-Drug Interaction Extraction (Yang et al., Louhi 2022)

ACL

• Jie Yang, Yihao Ding, Siqu Long, Josiah Poon, and Soyeon Caren Han. 2022. DDI-MuG: Multi-aspect Graphs for Drug-Drug Interaction Extraction. In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI), pages 127–137, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.