amed entity recognition (NER) is an important

task in natural language processing area,

which needs to determine entities boundaries

and classify them into pre-defined categories.

For Chinese NER task, there is only a very small

amount of annotated data available. Chinese

NER task and Chinese word segmentation

(CWS) task have many similar word

boundaries. There are also specificities in each

task. However, existing methods for Chinese

NER either do not exploit word boundary information

from CWS or cannot filter the specific

information of CWS. In this paper, we

propose a novel adversarial transfer learning

framework to make full use of task-shared

boundaries information and prevent the taskspecific

features of CWS. Besides, since arbitrary

character can provide important cues

when predicting entity type, we exploit selfattention

to explicitly capture long range dependencies

between two tokens. Experimental

results on two different widely used datasets

show that our proposed model significantly

and consistently outperforms other state-ofthe-

art methods.