We use cookies to improve your experience with our site.
Hong-Tao Zhang, Min-Lie Huang, Xiao-Yan Zhu. A Unified Active Learning Framework for Biomedical Relation Extraction[J]. Journal of Computer Science and Technology, 2012, 27(6): 1302-1313. DOI: 10.1007/s11390-012-1306-0
Citation: Hong-Tao Zhang, Min-Lie Huang, Xiao-Yan Zhu. A Unified Active Learning Framework for Biomedical Relation Extraction[J]. Journal of Computer Science and Technology, 2012, 27(6): 1302-1313. DOI: 10.1007/s11390-012-1306-0

A Unified Active Learning Framework for Biomedical Relation Extraction

  • Supervised machine learning methods have been employed with great success in the task of biomedical relation extraction. However, existing methods are not practical enough, since manual construction of large training data is very expensive. Therefore, active learning is urgently needed for designing practical relation extraction methods with little human effort. In this paper, we describe a unified active learning framework. Particularly, our framework systematically addresses some practical issues during active learning process, including a strategy for selecting informative data, a data diversity selection algorithm, an active feature acquisition method, and an informative feature selection algorithm, in order to meet the challenges due to the immense amount of complex and diverse biomedical text. The framework is evaluated on protein- protein interaction (PPI) extraction and is shown to achieve promising results with a significant reduction in editorial effort and labeling time.
  • loading

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return