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BERN2: an advanced neural biomedical named entity recognition and normalization tool.

Mujeen Sung1, Minbyul Jeong1, Yonghwa Choi1

  • 1Department of Computer Science and Engineering, Korea University, Seoul 02841, Republic of Korea.

Bioinformatics (Oxford, England)
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Summary

BERN2 is an advanced tool for biomedical named entity recognition (NER) and normalization (NEN). It uses multi-task and neural network models for faster, more accurate extraction of biomedical entities from literature.

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Area of Science:

  • Biomedical Natural Language Processing
  • Computational Biology
  • Bioinformatics

Background:

  • Named Entity Recognition (NER) and Named Entity Normalization (NEN) are crucial for extracting biomedical entities from vast scientific literature.
  • Existing tools often face limitations in speed and accuracy for large-scale text annotation.

Purpose of the Study:

  • To introduce BERN2, an improved tool for biomedical NER and NEN.
  • To enhance the speed and accuracy of extracting biomedical entities like diseases and drugs.

Main Methods:

  • Utilizes a multi-task neural network model for NER.
  • Employs neural network-based models for NEN.
  • Achieves faster and more accurate inference compared to previous methods.

Main Results:

  • Demonstrates significant improvements in both speed and accuracy for biomedical entity recognition and normalization.
  • Enables efficient annotation of large-scale biomedical texts.

Conclusions:

  • BERN2 offers a powerful solution for biomedical text annotation.
  • The tool facilitates tasks such as biomedical knowledge graph construction.