Abstract: Aiming at the problem that there was no obvious space separation between words in Chinese, which led to unclear vocabulary  boundaries, and it was difficult to accurately capture the relationship between entities and surrounding words, resulting in low  accuracy of Chinese named entity recognition, we proposed  a  fine-tuning optimization method of Chinese named entity 
recognition based on XLM-RoBERTa-Large-Finetuned-Conll03-English model combined with conditional random field (CRF). Firstly, we  established a Chinese named entity indicator lexicon, determined the scope of named entities,  sorted the entities, and used probability calculation to obtain the optimal features of named entities. Secondly, we introduced the features obtained by CRF into the XLM-RoBERTa-Large-Finetuned-Conll03-English model to capture the feature sequences of named entities and their dependencies. Finally, by adding CRF layer to the multi-language model, the fine-tuning optimization of Chinese named entity recognition was realized. The experimental results show that this fine-tuning optimization method significantly improves the performance of Chinese named entity recognition, enabling the model to have higher accuracy and lower loss value, and  better applicability in Chinese named entity recognition (NER) task.

Key words: XLM-RoBERTa model, named entity recognition, fine-tuning optimization, conditional random field