Paper: FBK-irst : A Multi-Phase Kernel Based Approach for Drug-Drug Interaction Detection and Classification that Exploits Linguistic Information

ACL ID S13-2057
Title FBK-irst : A Multi-Phase Kernel Based Approach for Drug-Drug Interaction Detection and Classification that Exploits Linguistic Information
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

This paper presents the multi-phase relation extraction (RE) approach which was used for the DDI Extraction task of SemEval 2013. As a preliminary step, the proposed approach in- directly (and automatically) exploits the scope of negation cues and the semantic roles of in- volved entities for reducing the skewness in the training data as well as discarding possible negative instances from the test data. Then, a state-of-the-art hybrid kernel is used to train a classifier which is later applied on the in- stances of the test data not filtered out by the previous step. The official results of the task show that our approach yields an F-score of 0.80 for DDI detection and an F-score of 0.65 for DDI detection and classification. Our sys- tem obtained significantly higher results than all the o...