Paper: WBI-DDI: Drug-Drug Interaction Extraction using Majority Voting

ACL ID S13-2105
Title WBI-DDI: Drug-Drug Interaction Extraction using Majority Voting
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

This work describes the participation of the WBI-DDI team on the SemEval 2013 ? Task 9.2 DDI extraction challenge. The task con- sisted of extracting interactions between pairs of drugs from two collections of documents (DrugBank and MEDLINE) and their clas- sification into four subtypes: advise, effect, mechanism, and int. We developed a two-step approach in which pairs are initially extracted using ensembles of up to five different clas- sifiers and then relabeled to one of the four categories. Our approach achieved the sec- ond rank in the DDI competition. For interac- tion detection we achieved F1 measures rang- ing from 73 % to almost 76 % depending on the run. These results are on par or even higher than the performance estimation on the train- ing dataset. When considering the four ...