Disease named entity recognition from biomedical literature using a novel convolutional neural network.

Journal: BMC medical genomics
Published Date: Dec 28, 2017

Abstract

BACKGROUND: Automatic disease named entity recognition (DNER) is of utmost importance for development of more sophisticated BioNLP tools. However, most conventional CRF based DNER systems rely on well-designed features whose selection is labor intensive and time-consuming. Though most deep learning methods can solve NER problems with little feature engineering, they employ additional CRF layer to capture the correlation information between labels in neighborhoods which makes them much complicated.

Authors

  • Zhehuan Zhao
    College of Computer Science and Technology, Dalian University of Technology, Dalian, China.
  • Zhihao Yang
    College of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China.
  • Ling Luo
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Lei Wang
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Yin Zhang
    Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States.
  • Hongfei Lin
  • Jian Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.

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