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.

命名实体识别在医学自然语言处理中起着关键作用。本研究利用 ChatGPT 构建了 KBMC（韩国生物医学语料库），与一般韩国 NER 数据集相比，在 KBMC 数据集上训练的模型在医学 NER 任务中取得了令人印象深刻的 20% 性能提升。这项研究强调了在专业领域如医疗保健中使用类似 ChatGPT 这样的专用工具和数据集来增强语言处理的重大益处和重要性。