摘要
Abstract
To improve the intelligence level of sensitive information management work,this paper proposes a BERT-BGRU-CRF Deep Learning method to achieve automatic recognition of sensitive information.This method first preprocesses the text information using the BERT model,then uses the Bidirectional Gated Recurrent Unit(BGRU)model to obtain contextual semantic features,and finally inputs the extracted information into the Conditional Random Field model for sequence annotation to obtain the optimal solution.The experimental results show that on the self-built dataset,the proposed method achieves higher scores in accuracy,recall,and F1 value compared to the three recognition methods BERT-CRF,BERT-LSTM-CRF,and BERT-BiLSTM-CRF,proving that this method is suitable for intelligent identification of sensitive information.关键词
敏感信息识别/深度学习/门控循环单元/BERT/条件随机场Key words
sensitive information recognition/Deep Learning/Gated Recurrent Unit/BERT/Conditional Random Field分类
信息技术与安全科学
