  We propose a new Named entity recognition (NER) method to effectively make
use of the results of Part-of-speech (POS) tagging, Chinese word segmentation
(CWS) and parsing while avoiding NER error caused by POS tagging error. This
paper first uses Stanford natural language process (NLP) tool to annotate
large-scale untagged data so as to reduce the dependence on the tagged data;
then a new NLP model, g-BERT model, is designed to compress Bidirectional
Encoder Representations from Transformers (BERT) model in order to reduce
calculation quantity; finally, the model is evaluated based on Chinese NER
dataset. The experimental results show that the calculation quantity in g-BERT
model is reduced by 60% and performance improves by 2% with Test F1 to 96.5
compared with that in BERT model.
