中文命名实体识别

In response to the current problems of inadequate and incomplete semantic feature extraction in Chinese named entity recognition research, Transformers (BERT) has shown striking improvements in a variety of related NLP tasks, and successive variants have been proposed to further improve the performance<br />of pre-trained language models. In this paper, our goal is to revisit Chinese pre-trained language models to examine their effectiveness in non-English languages. This paper is based on the RoBERT model for fine-tuning, and experimental results show good performance on many NLP tasks.