CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
作者: Wonjin YoonChan Ho SoJinhyuk LeeJaewoo Kang
刊名: BMC Bioinformatics, 2019, Vol.20 (S10), pp.55-65
来源数据库: Directory of Open Access Journals
DOI: 10.1186/s12859-019-2813-6
关键词: NERDeep learningNamed entity recognitionText mining
原始语种摘要: 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. Results To address the lack of data and the entity type misclassification problem, we propose CollaboNet which utilizes a combination of multiple NER models. In CollaboNet, models trained on a different dataset are...
全文获取路径: DOAJ  (合作)
影响因子:3.024 (2012)

  • recognition 识别
  • biomedical 生物医学
  • named 命名
  • entity 实质
  • neural 神经系统的
  • mining 矿业
  • performance 性能
  • multiple 多次的
  • learning 学识
  • collaboration 协作