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SCAI: Extracting drug-drug interactions using a rich feature vector

: Bobic, Tamara; Fluck, Juliane; Hofmann-Apitius, Martin

Fulltext urn:nbn:de:0011-n-2703811 (187 KByte PDF)
MD5 Fingerprint: 58038e488c2d2ec3f80c509033db2663
Created on: 10.12.2013

Association for Computational Linguistics -ACL-:
*SEM 2013, Second Joint Conference on Lexical and Computational Semantics. Vol.2: Proceedings of the Seventh International Workshop on Semantic Evaluation, SemEval 2013 : Atlanta, Georgia, June 14-15, 2013
Madison/Wis.: Omnipress, 2013
ISBN: 978-1-937284-49-7 (Vol.2)
Conference on Lexical and Computational Semantics (*SEM) <2, 2013, Atlanta/Ga.>
InternationalWorkshop on Semantic Evaluation (SemEval) <7, 2013, Atlanta/Ga.>
Conference Paper, Electronic Publication
Fraunhofer SCAI ()
text mining; relation extraction; drug drug interaction

Automatic relation extraction provides great support for scientists and database curators in dealing with the extensive amount of biomedical textual data. The DDIExtraction 2013 challenge poses the task of detecting drug drug interactions and further categorizing them into one of the four relation classes. We present our machine learning system which utilizes lexical, syntactical and semantic based feature sets. Resampling, balancing and ensemble learning experiments are performed to infer the best configuration. For general drug drug relation extraction, the system achieves 70.4% in F1 score.