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Biomedical named entity recognition using deep neural networks with contextual information.

Hyejin Cho1, Hyunju Lee2

  • 1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Chemdangwagi-ro, Buk-gu, Gwangju, Republic of Korea.

BMC Bioinformatics
|December 29, 2019
PubMed
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This study introduces a new named entity recognition (NER) system, contextual long short-term memory networks with CRF (CLSTM), for biomedical text mining. The CLSTM model demonstrates robust performance in recognizing biological entities, outperforming previous methods on key datasets.

Area of Science:

  • Biomedical text mining
  • Natural Language Processing
  • Bioinformatics

Background:

  • Traditional named entity recognition (NER) methods rely heavily on dictionaries, rules, or extensive corpora.
  • Deep learning approaches have advanced NER but often fail to fully integrate contextual text information.
  • Existing methods may not capture all relevant correlations between labels in biomedical text.

Purpose of the Study:

  • To develop an improved named entity recognition (NER) system for biomedical entities.
  • To enhance the capture of contextual information in deep learning models for NER.
  • To create a robust system for extracting biological entities from scientific literature.

Main Methods:

  • Proposed a novel NER system named contextual long short-term memory networks with CRF (CLSTM).
Keywords:
Contextual informationLong short-term memoryNamed entity recognitionNeural networksText mining

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  • Incorporated n-grams with bi-directional long short-term memory (BiLSTM) and CRF to capture contextual information.
  • Evaluated the CLSTM model on diverse biomedical corpora, including disease, gene, and chemical-disease relation datasets.

    • Main Results:

    • The CLSTM model achieved an F-score of 85.68% for disease NER on the NCBI corpus, improving by 1.50%.
    • Demonstrated comparable performance to BERT on gene NER (F-score of 81.44% on GM corpus).
    • Outperformed existing methods on chemical and disease NER (F-score of 86.44% on CDR corpus).

    Conclusions:

    • The proposed CLSTM approach significantly enhances performance in biomedical NER tasks.
    • The system proves robust and effective in recognizing various biological entities within text.
    • This method offers a valuable advancement for information extraction in the biomedical domain.