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Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition

Sungjoo Byun, Jiseung Hong, Sumin Park, Dongjun Jang, Jean Seo, Minseok Kim, Chaeyoung Oh, Hyopil Shin

TL;DR

This research underscores the significant benefits and importance of using specialized tools and datasets, like ChatGPT, to enhance language processing in specialized fields such as healthcare.

Abstract

Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP). Yet, there has not been an open-source medical NER dataset specifically for the Korean language. To address this, we utilized ChatGPT to assist in constructing the KBMC (Korean Bio-Medical Corpus), which we are now presenting to the public. With the KBMC dataset, we noticed an impressive 20% increase in medical NER performance compared to models trained on general Korean NER datasets. This research underscores the significant benefits and importance of using specialized tools and datasets, like ChatGPT, to enhance language processing in specialized fields such as healthcare.

Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition

TL;DR

This research underscores the significant benefits and importance of using specialized tools and datasets, like ChatGPT, to enhance language processing in specialized fields such as healthcare.

Abstract

Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP). Yet, there has not been an open-source medical NER dataset specifically for the Korean language. To address this, we utilized ChatGPT to assist in constructing the KBMC (Korean Bio-Medical Corpus), which we are now presenting to the public. With the KBMC dataset, we noticed an impressive 20% increase in medical NER performance compared to models trained on general Korean NER datasets. This research underscores the significant benefits and importance of using specialized tools and datasets, like ChatGPT, to enhance language processing in specialized fields such as healthcare.
Paper Structure (12 sections, 1 equation, 3 figures, 5 tables)

This paper contains 12 sections, 1 equation, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. KBMC : Korean Bio-Medical Corpus
  4. Data Construction
  5. Annotation Result
  6. Data Application
  7. KBMC
  8. Models
  9. Results
  10. KBMC Applicability Assessment
  11. Conclusion
  12. Appendix

Figures (3)

  • Figure 1: Construction Process of KBMC
  • Figure 2: KBMC Annotation
  • Figure 3: The distribution of Named Entity labels in two datasets: the original Naver NER dataset (left), and a combined version of the Naver NER dataset (partial) and KBMC (right). The original Naver dataset contains the label TRM, representing medical and IT-related terms. In the combined dataset, sentences that include TRM from the original dataset have been replaced with data from KBMC, aiming to achieve a more accurate classification of medical terms into refined categories.