FBK-irst : A Multi-Phase Kernel Based Approach for Drug-Drug Interaction Detection and Classification that Exploits Linguistic Information
Md. Faisal Mahbub Chowdhury, Alberto Lavelli · Joint Conference on Lexical and Computational Semantics · 2013
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 indirectly (and automatically) exploits the scope of negation cues and the semantic roles of involved 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 instances 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 system obtained significantly higher results than all the other participating teams in this shared task and has been ranked 1st.