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. 2022 Oct 14;38(20):4837-4839.
doi: 10.1093/bioinformatics/btac598.

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

Mujeen Sung  1 , Minbyul Jeong  1 , Yonghwa Choi  1 , Donghyeon Kim  2 , Jinhyuk Lee  1 , Jaewoo Kang  1   3
Affiliations

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

Mujeen Sung et al. Bioinformatics. .

Abstract

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.

Availability and implementation: Web service of BERN2 is publicly available at http://bern2.korea.ac.kr. We also provide local installation of BERN2 at https://github.com/dmis-lab/BERN2.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
An overview of BERN2. Given plain text or a PubMed ID (PMID), BERN2 recognizes nine biomedical entity types and normalizes each concept
See this image and copyright information in PMC

References

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