Interactive medical word sense disambiguation through informed learning.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jul 1, 2018

Abstract

OBJECTIVE: Medical word sense disambiguation (WSD) is challenging and often requires significant training with data labeled by domain experts. This work aims to develop an interactive learning algorithm that makes efficient use of expert's domain knowledge in building high-quality medical WSD models with minimal human effort.

Authors

  • Yue Wang
    Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Kai Zheng
    University of California, Irvine, Irvine, CA, USA.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Qiaozhu Mei
    University of Michigan, Ann Arbor, MI.

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