UColorado_SOM: Extraction of Drug-Drug Interactions from Biomedical Text using Knowledge-rich and Knowledge-poor Features

Abstract

In this paper, we present our approach to SemEval-2013 Task 9.2. It is a feature rich classification using LIBSVM for Drug-Drug Interactions detection in the BioMedical domain. The features are extracted considering morphosyntactic, lexical and semantic concepts. Tools like openDMAP and TEES are used to extract semantic concepts from the corpus. The best F-score that we got for Drug-Drug Interaction (DDI) detection is 50 % and 61 % and the best F-score for DDI detection and classification is 34 % and 48 % for test and development data respectively.