Document-level attention-based BiLSTM-CRF incorporating disease dictionary for disease named entity recognition.

Journal: Computers in biology and medicine
Published Date: Apr 7, 2019

Abstract

BACKGROUND: Disease named entity recognition (NER) plays an important role in biomedical research. There are a significant number of challenging issues to be addressed; among these, the identification of rare diseases and complex disease names and the problem of tagging inconsistency (i.e., if an entity is tagged differently in a document) are attracting substantial research attention.

Authors

  • Kai Xu
    Department of Anesthesiology, Huai'an Hospital Affiliated to Yangzhou University (The Fifth People's Hospital of Huai'an), Huaian, China.
  • Zhenguo Yang
    Department of Computer Science, Guangdong University of Technology, Guangzhou, China; Department of Computer Science, City University of Hong Kong, Hong Kong, China. Electronic address: zhengyang5-c@my.cityu.edu.hk.
  • Peipei Kang
    Department of Anesthesiology, Affiliated Tumor Hospital of Nantong University & Nantong Tumor Hospital, Nantong, China.
  • Qi Wang
    Biotherapeutics Discovery Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Wenyin Liu
    School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China. liuwy@gdut.edu.cn.

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