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

Mujeen Sung, Minbyul Jeong, Yonghwa Choi, Donghyeon Kim, Jinhyuk Lee, Jaewoo Kang

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e.g. diseases and drugs) from the ever-growing biomedical literature. In this article, we present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by employing a multi-task NER model and neural network-based NEN models to achieve much faster and more accurate inference. We hope that our tool can help annotate large-scale biomedical texts for various tasks such as biomedical knowledge graph construction.

Original languageEnglish
Pages (from-to)4837-4839
Number of pages3
Issue number20
Publication statusPublished - 2022 Oct 15

Bibliographical note

Funding Information:
This work was supported in part by National Research Foundation of Korea (NRF-2020R1A2C3010638, NRF-2014M3C9A3063541), the Ministry of Health & Welfare, Republic of Korea (HR20C0021) and the ICT Creative Consilience program (IITP-2021-0-01819) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).

Publisher Copyright:
© 2022 The Author(s).

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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