A Model for Chinese Named Entity Recognition Based on Global Pointer and Adversarial Learning  

作　　者：ZHANG Yangsen LI Jianlong XIN Yonghui ZHAO Xiquan LIU Yang 

机构地区：[1]Institute of Intelligent Information Processing,Beijing Information Science and Technology University,Beijing 100192,China [2]Computer Network Emergency Response Technical Team,Coordination Center of China,Beijing 100029,China

出　　处：《Chinese Journal of Electronics》2023年第4期854-867,共14页电子学报（英文版）

基　　金：This work was supported by the National Natural Science Foundation of China(61772081).

摘　　要：To solve the problem that the Chinese named entity recognition(NER)models have poor antiinterference ability and inaccurate entity boundary recognition,this paper proposes the RGP-with-FGM model which is based on global pointer and adversarial learning.Firstly,the RoBERTa-WWM model is used to optimize the semantic representation of the text,and fast gradient method is used to add perturbation to the word embedding layer to enhance the robustness of the model.Then,BiGRU is used to focus on the timing information of Chinese characters to enhance the semantic connection.Finally,the global pointer is constructed in the decoding layer to obtain more accurate entity boundary recognition results.In order to verify the effectiveness of the model proposed in this paper,we construct Uyghur names dataset(UHND)to train the Chinese NER model,and perform extensive experiments with public Chinese NER data sets.Experimental results show that for UHND,the F1 value is 95.12%,which is 3.09%higher than that of the RoBERTa-WWM-BiGRU-CRF model.For the Resume data set,the Precision and F1 value are 96.28%and 96.10%respectively.

关 键 词：Chinese named entity recognition(NER) Global pointer RoBERTa-WWM model Fast gradient method(FGM) BiGRU 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]