Paper: UTurku: Drug Named Entity Recognition and Drug-Drug Interaction Extraction Using SVM Classification and Domain Knowledge

ACL ID S13-2108
Title UTurku: Drug Named Entity Recognition and Drug-Drug Interaction Extraction Using SVM Classification and Domain Knowledge
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

The DDIExtraction 2013 task in the SemEval conference concerns the detection of drug names and statements of drug-drug interac- tions (DDI) from text. Extraction of DDIs is important for providing up-to-date knowl- edge on adverse interactions between co- administered drugs. We apply the machine learning based Turku Event Extraction Sys- tem to both tasks. We evaluate three fea- ture sets, syntactic features derived from deep parsing, enhanced optionally with features de- rived from DrugBank or from both DrugBank and MetaMap. TEES achieves F-scores of 60% for the drug name recognition task and 59% for the DDI extraction task.