@inproceedings{d7af2023135043f68bd91108e32ded84,
title = "Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble",
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 discovering. 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.",
author = "Tu, {Jing Cyun} and Lai, {Po Ting} and Tsai, {Richard Tzong Han}",
note = "Publisher Copyright: {\textcopyright} 2017 AFNLP; 1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017 ; Conference date: 27-11-2017",
year = "2017",
language = "???core.languages.en_GB???",
series = "DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop",
publisher = "Association for Computational Linguistics (ACL)",
pages = "52--56",
booktitle = "DDDSM 2017 - 1st International Workshop on Digital Disease Detection using Social Media, Proceedings of the Workshop",
}