Extraction of Regulatory Events using Kernel-based Classifiers and Distant Supervision
André Lamúrias, Miguel J. Rodrigues, Luka A. Clarke, Francisco M. Couto · 2016
This paper describes our system to extract binary regulatory relations from text, used to participate in the SeeDev task of BioNLP-ST 2016.Our system was based on machine learning, using support vector machines with a shallow linguistic kernel to identify each type of relation.Additionally, we employed a distant supervised approach to increase the size of the training data.Our submission obtained the third best precision of the SeeDev-binary task.Although the distant supervised approach did not significantly improve the results, we expect that by exploring other techniques to use unlabeled data should lead to better results.