A More Efficient Chinese Named Entity Recognition base on BERT and Syntactic Analysis

TL;DR

This paper introduces a more efficient Chinese NER method that leverages syntactic analysis and a compressed BERT model, achieving higher accuracy with significantly reduced computational cost.

Contribution

It presents a novel g-BERT model that compresses BERT for Chinese NER, utilizing syntactic information to improve performance and efficiency.

Findings

g-BERT reduces calculation by 60%

NER performance improves by 2% F1 score

Method effectively utilizes POS, CWS, and parsing results

Abstract

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.