Chinese HealthNER Corpus是由NYCU NLP Lab收集和标注的医疗命名实体识别数据集。该数据集首先从提供医疗信息的网站、在线健康相关新闻和医疗问答论坛中爬取文章，然后去除所有HTML标签、图像、视频和嵌入的网络广告，并将剩余文本分割成多个句子。数据集包含了10种实体类型，如人体、症状、医疗器材等，并由三名中文专业的本科生进行标注，标注一致性达到84.1%。
The SocialDisNER corpus of the SMM4H 2022 – Task 10 track was manually annotated by medical experts following the SMM4H-SocialDisNER guidelines. These guidelines were adapted from pre
