Abstract: Existing Chinese named entity recognition algorithms inadequately consider the data features of entity recognition tasks, leading to imbalance in the categories of Chinese sample data, excessive noise in the training data, and significant differences in the distribution of generated data. An improved Chinese named entity recognition model based on BERT-BiLSTM-CRF ( Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field) is proposed. The first improvement involves combining the P-Tuning v2 technology with BERT-BiLSTM-CRF to accurately extract data features. And three
loss functions, including Focal Loss, Label Smoothing, and KL Loss(Kullback-Leibler divergence loss), are utilized as regularization terms in the loss calculation to address the problems. The improved model achieves F1 scores of 71. 13% ,96. 31% , and 95. 90% on the Weibo, Resume, and MSRA( Microsoft Research Asia)datasets, respectively. The results validate that the proposed algorithm outperforms previous research achievements in terms of performance and is easy to combine and extend with other neural networks for various downstream tasks.

Key words: Chinese named entity recognition, bidirectional encoder representations from transformers-bidirectional long short-term memory-conditional random field ( BERT-BiLSTM-CRF ) model, P-tuning v2 technology, loss function