吉林大学学报（信息科学版）2025，Vol.43Issue(2)：384-393,10.
基于Bert-BiLSTM-CRF模型的中文命名实体识别
Chinese Named Entity Recognition Based on BERT-BiLSTM-CRF
摘要
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.关键词
中文命名实体识别/BERT-BiLSTM-CRF模型/P-Tuning v2技术/损失函数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分类
信息技术与安全科学引用本文复制引用
龙星全,李佳..基于Bert-BiLSTM-CRF模型的中文命名实体识别[J].吉林大学学报（信息科学版）,2025,43(2):384-393,10.基金项目
吉林省科技厅发展计划基金资助项目(20220101129JC) （20220101129JC）
