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Deep learning for named entity recognition: a survey

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Abstract

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.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their critical and constructive comments and suggestions. This work was supported in part by the National Natural Science Foundation of China under Grant 61976080, in part by the Academic Degrees and Graduate Education Reform Project of Henan Province under Grant 2021SJGLX195Y and in part by the Innovation and Quality Improvement Project for Graduate Education of Henan University under Grants SYL20010101, SYLYC2022191, and SYLYC2022192.

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    Zhentao Hu, Wei Hou & Xianxing Liu

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Hu, Z., Hou, W. & Liu, X. Deep learning for named entity recognition: a survey. Neural Comput & Applic 36, 8995–9022 (2024). https://doi.org/10.1007/s00521-024-09646-6

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  • DOI: https://doi.org/10.1007/s00521-024-09646-6

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