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

To solve the problem that the Chinese named entity recognition (NER) models have poor anti-interference 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.