Cross domains adversarial learning for Chinese named entity recognition for online medical consultation

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. [Display omitted] •A larger labelled database for Chinese named entity recognition.•The basic framework unit (BFU) composed of word vector, CNNs, BiLSTM, and CRF.•The larger texts database without labels are applied to pre-train BFU.•Combination of pre-trained BFU and adversarial multi-task learning.