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

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)4837-4839
Number of pages3
JournalBioinformatics (Oxford, England)
Volume38
Issue number20
DOIs
Publication statusPublished - 2022 Oct 14

ASJC Scopus subject areas

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

Fingerprint

Dive into the research topics of 'BERN2: an advanced neural biomedical named entity recognition and normalization tool'. Together they form a unique fingerprint.

Cite this