AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction and question answering. Manually labeling training data for the BioNER task is costly, however, due to the significant domain expertise required for accurate annotation. The resulting data scarcity causes current BioNER approaches to be prone to overfitting, to suffer from limited generalizability, and to address a single entity type at a time (e.g. gene or disease).

Authors

  • Ling Luo
    Department of Epidemiology and Medical Statistics School of Public Health, Guangdong Medical University, Dongguan, Guangdong, China.
  • Chih-Hsuan Wei
    National Center for Biotechnology Information, Bethesda, MD 20894 USA.
  • Po-Ting Lai
    National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
  • Robert Leaman
  • Qingyu Chen
    Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA.
  • Zhiyong Lu
    National Center for Biotechnology Information, Bethesda, MD 20894 USA.