Extraction of Regulatory Events using Kernel-based Classifiers and Distant Supervision

نویسندگان

  • Andre Lamurias
  • Miguel J. Rodrigues
  • Luka A. Clarke
  • Francisco M. Couto
چکیده

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.

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تاریخ انتشار 2016