Abstract: Named entity recognition (NER), as a core task in natural language processing, finds extensive applications in information extraction, question answering systems, machine translation, and more. Firstly, descriptions and summaries are provided for rule-based, dictionary-based, and statistical machine learning methods. Subsequently, an overview of NER models based on deep learning, including supervised, distant supervision, and Transformer-based approaches, is presented. Particularly, recent advancements in Transformer architecture and its related models in the field of natural language processing are elucidated, such as Transformer-based masked language modeling and autoregressive language modeling, including BERT, T5, and GPT. Furthermore, brief discussions are conducted on data transfer learning and model transfer learning methods applied to NER. Finally, challenges faced by NER tasks and future development trends are summarized.

Key words: named entity recognition, machine learning, deep learning, transfer learning, natural language processing