CollaboNet: collaboration of deep neural networks for biomedical named entity recognition.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results. However, as deep learning approaches need an abundant amount of training data, a lack of data can hinder performance. BioNER datasets are scarce resources and each dataset covers only a small subset of entity types. Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition.

Authors

  • Wonjin Yoon
    Department of Computer Science and Engineering, Korea University, Seoul, 02841, Republic of Korea.
  • Chan Ho So
    Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, 02841, Republic of Korea.
  • Jinhyuk Lee
    Department of Computer Science and Engineering, Korea University, Seoul, 02841, Republic of Korea.
  • Jaewoo Kang
    Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea.