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
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.