Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition
This provides a specialized dataset for Korean medical NLP, addressing a gap for researchers and practitioners in healthcare, though it is incremental as it applies existing methods to new data.
The authors tackled the lack of an open-source medical NER dataset for Korean by creating the Korean Bio-Medical Corpus (KBMC) using ChatGPT, resulting in a 20% increase in medical NER performance compared to models trained on general Korean datasets.
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.