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A Unified Active Learning Framework for Biomedical Relation Extraction

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Abstract

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.

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Authors and Affiliations

  1. State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Hong-Tao Zhang, Min-Lie Huang & Xiao-Yan Zhu

Authors
  1. Hong-Tao Zhang
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  2. Min-Lie Huang
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  3. Xiao-Yan Zhu
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Corresponding author

Correspondence to Hong-Tao Zhang.

Additional information

The work is supported by the National Natural Science Foundation of China under Grant No. 60973104 and the National Basic Research 973 Program of China under Grant No. 2012CB316301.

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Zhang, HT., Huang, ML. & Zhu, XY. A Unified Active Learning Framework for Biomedical Relation Extraction. J. Comput. Sci. Technol. 27, 1302–1313 (2012). https://doi.org/10.1007/s11390-012-1306-0

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  • DOI: https://doi.org/10.1007/s11390-012-1306-0

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