@InProceedings{tu-lai-tsai:2017:DDDSM,
  author    = {Tu, Jing Cyun  and  Lai, Po-Ting  and  Tsai, Richard Tzong-Han},
  title     = {Enhancing Drug-Drug Interaction Classification with Corpus-level Feature and Classifier Ensemble},
  booktitle = {Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Association for Computational Linguistics},
  pages     = {52--56},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-5808}
}