Abstract
The study of drug-drug interaction (DDI) is important in the drug discovering. Both PubMed and DrugBank are rich resources to retrieve DDI information which is usually represented in plain text. Automatically extracting DDI pairs from text improves the quality of drug discov-ering. In this paper, we presented a study that focuses on the DDI classification. We normalized the drug names, and developed both sentence-level and corpus-level features for DDI classification. A classifier ensemble approach is used for the unbalance DDI labels problem. Our approach achieved an F-score of 65.4% on SemEval 2013 DDI test set. The experimental results also show the effects of proposed corpus-level features in the DDI task.- Anthology ID:
- W17-5808
- Volume:
- Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Jitendra Jonnagaddala, Hong-Jie Dai, Yung-Chun Chang
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 52–56
- Language:
- URL:
- https://aclanthology.org/W17-5808
- DOI:
- Cite (ACL):
- Jing Cyun Tu, Po-Ting Lai, and Richard Tzong-Han Tsai. 2017. Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble. In Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017), pages 52–56, Taipei, Taiwan. Association for Computational Linguistics.
- Cite (Informal):
- Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble (Tu et al., 2017)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W17-5808.pdf
- Data
- DDI