Deep learning for named entity recognition: a survey

Named entity recognition (NER) aims to identify the required entities and their types from unstructured text, which can be utilized for the construction of knowledge graphs. Traditional methods heavily rely on manual feature engineering and face challenges in adapting to large datasets within complex linguistic contexts. In recent years, with the development of deep learning, a plethora of NER methods based on deep learning have emerged. This paper begins by providing a succinct introduction to the definition of the problem and the limitations of traditional methods. It enumerates commonly used NER datasets suitable for deep learning methods and categorizes them into three classes based on the complexity of named entities. Then, some typical deep learning-based NER methods are summarized in detail according to the development history of deep learning models. Subsequently, an in-depth analysis and comparison of methods achieving outstanding performance on representative and widely used datasets is conducted. Furthermore, the paper reproduces and analyzes the recognition results of some typical models on three different types of typical datasets. Finally, the paper concludes by offering insights into the future trends of NER development. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.