Cross domains adversarial learning for Chinese named entity recognition for online medical consultation.
J Biomed Inform
; 112: 103608, 2020 12.
Article
en En
| MEDLINE
| ID: mdl-33132138
ABSTRACT
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Lenguaje Natural
/
Lenguaje
Tipo de estudio:
Prognostic_studies
País/Región como asunto:
Asia
Idioma:
En
Revista:
J Biomed Inform
Asunto de la revista:
INFORMATICA MEDICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
China