Recent Progress on Named Entity Recognition Based on Pre-trained Language Models

This paper presents an overview of the latest developments in Named Entity Recognition (NER) using Pretrained Language Models (PLMs). First, it discusses how PLMs have evolved and their impact on NER, including various pretraining techniques. The paper then explores strategies to adapt PLMs to NER, such as fine-tuning and transfer learning, while also considering the challenges in selecting the suitable model. Recent advancements in NER techniques using PLMs, including integrating external knowledge sources, are also covered. Finally, the paper comprehensively evaluates the state-of-the-art NER systems based on PLMs, comparing their performance and discussing their strengths and limitations. Its goal is to provide a thorough understanding of NER progress, insights into ongoing advancements, and potential avenues for further exploration. This paper is a valuable resource for researchers and practitioners seeking to use PLMs in their NER applications.