WBI-DDI: Drug-Drug Interaction Extraction using Majority Voting
نویسندگان
چکیده
This work describes the participation of the WBI-DDI team on the SemEval 2013 – Task 9.2 DDI extraction challenge. The task consisted of extracting interactions between pairs of drugs from two collections of documents (DrugBank and MEDLINE) and their classification 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 classifiers and then relabeled to one of the four categories. Our approach achieved the second rank in the DDI competition. For interaction detection we achieved F1 measures ranging from 73 % to almost 76 % depending on the run. These results are on par or even higher than the performance estimation on the training dataset. When considering the four interaction subtypes we achieved an F1 measure of 60.9 %.
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