A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining

Donghyeon Kim, Jinhyuk Lee, Chan Ho So, Hwisang Jeon, Minbyul Jeong, Yonghwa Choi, Wonjin Yoon, Mujeen Sung, Jaewoo Kang

Research output: Contribution to journalArticlepeer-review

86 Citations (Scopus)


The amount of biomedical literature is vast and growing quickly, and accurate text mining techniques could help researchers to efficiently extract useful information from the literature. However, existing named entity recognition models used by text mining tools such as tmTool and ezTag are not effective enough, and cannot accurately discover new entities. Also, the traditional text mining tools do not consider overlapping entities, which are frequently observed in multi-type named entity recognition results. We propose a neural biomedical named entity recognition and multi-type normalization tool called BERN. The BERN uses high-performance BioBERT named entity recognition models which recognize known entities and discover new entities. Also, probability-based decision rules are developed to identify the types of overlapping entities. Furthermore, various named entity normalization models are integrated into BERN for assigning a distinct identifier to each recognized entity. The BERN provides a Web service for tagging entities in PubMed articles or raw text. Researchers can use the BERN Web service for their text mining tasks, such as new named entity discovery, information retrieval, question answering, and relation extraction. The application programming interfaces and demonstrations of BERN are publicly available at https://bern.korea.ac.kr.

Original languageEnglish
Article number8730332
Pages (from-to)73729-73740
Number of pages12
JournalIEEE Access
Publication statusPublished - 2019

Bibliographical note

Funding Information:
This work was supported in part by the National Research Foundation of Korea under Grant NRF-2017R1A2A1A17069645 and Grant NRF-2016M3A9A7916996, and in part by the National IT Industry Promotion Agency, Development Project of the Precision Medicine Hospital Information System (P-HIS), under Grant C1202-18-1001.

Publisher Copyright:
© 2013 IEEE.


  • Biomedical text mining
  • Web service
  • decision rules
  • multi-type
  • named entity recognition
  • neural networks
  • normalization

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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