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

ACL ID S13-2112
Title UColorado_SOM: Extraction of Drug-Drug Interactions from Biomedical Text using Knowledge-rich and Knowledge-poor Features
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

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 do- main. The features are extracted considering morphosyntactic, lexical and semantic con- cepts. 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. Keywords: text mining, event extraction, ma- chine learning, feature extraction.