Deep learning methods have been applied to Chinese named entity recognition for the online medical consultation. They require a large number of marked samples. However, no such database is available at present. This paper begins with constructing a larger labelled Chinese texts database for the online medical consultation. Second, a basic framework unit is proposed, which is pre-trained by the transfer learning from both Bidirectional language model and Mask language model trained on the larger unlabelled data. Finally, cross domains adversarial learning (CDAL) for Chinese named entity recognition is proposed to further improve the performance, which not only uses the pre-trained basic framework unit, but also uses the adversarial multi-task learning on both electronic medical record texts and online medical consultation texts. Experimental results validate the effectiveness of CDAL.

深度学习方法已应用于中文命名实体识别以进行在线医疗咨询。他们需要大量标记的样品。但是，目前没有这样的数据库。本文首先构建一个更大的带有标签的中文文本数据库，以进行在线医疗咨询。其次，提出了一个基本框架单元，该框架单元是通过在较大的未标记数据上训练的双向语言模型和掩码语言模型的传递学习来进行预训练的。最后，提出了用于中文命名实体识别的跨域对抗学习（CDAL），以进一步提高性能，它不仅使用了预先训练的基本框架单元，而且还使用了对电子病历文本和文档的对抗多任务学习。在线医疗咨询文本。