A Chinese named entity recognition model based on multi-feature fusion embedding
In order to solve the problems of differences in Chinese glyphs and blurred boundaries of Chinese words,a Chinese named entity recognition model based on multi-feature fusion embedding is proposed.On the basis of extracting semantic features,glyph features are captured based on convolu-tional neural network and multi-headed self-attention mechanism,word features are obtained with refer-ence to the word vector embedding table,and the bidirectional long short-term memory neural network is used to learn the context representation of long distance.Finally the constraint conditions in sentence sequence labels are learned by combining the conditional random field to realize Chinese named entity recognition.The Fl values on the Resume,Weibo and People Daily datasets reach 96.66％,70.84％and 96.15％,respectively,which proves that the proposed model effectively improves the performance of Chinese named entity recognition tasks.
named entity recognitionfeature fusionmulti-headed self-attention mechanism
国家科技期刊平台
NSTL
万方数据
