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A Survey on Deep Learning for Named Entity Recognition

arXiv 1812.09449 ↗First posted 22 Dec 2018cs.CLcited by 281

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Abstract (arXiv)

Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language applications such as question answering, text summarization, and machine translation. Early NER systems got a huge success in achieving good performance with the cost of human engineering in designing domain-specific features and rules. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In this paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area.

Key facts. Cited by 281 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Dec 2018.

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Citing papers per year: 2021: 29; 2022: 47; 2023: 71; 2024: 61; 2025: 50; 2026: 23.

281 papers.

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  1. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 123
  2. 2024AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulatorcited by 99
  3. 2024Revolutionizing Finance with LLMs: An Overview of Applications and Insightscited by 88
  4. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 86
  5. 2021Video Summarization Using Deep Neural Networks: A Surveycited by 85
  6. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
  7. 2023DocILE Benchmark for Document Information Localization and Extractioncited by 58
  8. 2021Named Entity Recognition and Classification on Historical Documents: A Surveycited by 47
  9. 2023Uncertainty in Natural Language Processing: Sources, Quantification, and Applicationscited by 44
  10. 2024Y Social: an LLM-powered Social Media Digital Twincited by 43
  11. 2021Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimatescited by 39
  12. 2023XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languagescited by 34
38 more
  1. 2024A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directionscited by 34
  2. 2022PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptationcited by 31
  3. 2025LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs -- No Silver Bullet for LC or RAG Routingcited by 29
  4. 2023Zero-shot cross-lingual transfer language selection using linguistic similaritycited by 28
  5. 2023CodeKGC: Code Language Model for Generative Knowledge Graph Constructioncited by 28
  6. 2022A Survey on Neural Open Information Extraction: Current Status and Future Directionscited by 27
  7. 2022Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTIcited by 27
  8. 2023Training-free Lexical Backdoor Attacks on Language Modelscited by 27
  9. 2022Named Entity Recognition in Indian court judgmentscited by 26
  10. 2025AutoData: A Multi-Agent System for Open Web Data Collectioncited by 25
  11. 2021DeepSTL -- From English Requirements to Signal Temporal Logiccited by 22
  12. 2022A Survey of Adversarial Defences and Robustness in NLPcited by 22
  13. 2021Entity Linking Meets Deep Learning: Techniques and Solutionscited by 21
  14. 2024Leveraging Large Language Model as Simulated Patients for Clinical Educationcited by 20
  15. 2022E2S2: Encoding-Enhanced Sequence-to-Sequence Pretraining for Language Understanding and Generationcited by 19
  16. 2024CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programmingcited by 19
  17. 2024GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understandingcited by 18
  18. 2022Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systemscited by 17
  19. 2024A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluationscited by 16
  20. 2023Zero-shot information extraction from radiological reports using ChatGPTcited by 15
  21. 2021DEAP-FAKED: Knowledge Graph based Approach for Fake News Detectioncited by 13
  22. 2022A Survey on Backdoor Attack and Defense in Natural Language Processingcited by 13
  23. 2022E-NER -- An Annotated Named Entity Recognition Corpus of Legal Textcited by 13
  24. 2021On the Use of External Data for Spoken Named Entity Recognitioncited by 12
  25. 2023Construction of Knowledge Graphs: State and Challengescited by 12
  26. 2024Human-inspired Perspectives: A Survey on AI Long-term Memorycited by 12
  27. 2021A Light-weight contextual spelling correction model for customizing transducer-based speech recognition systemscited by 11
  28. 2021TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labelscited by 11
  29. 2022Domain-Specific NER via Retrieving Correlated Samplescited by 11
  30. 2023Continual Named Entity Recognition without Catastrophic Forgettingcited by 11
  31. 2022Information Extraction in Low-Resource Scenarios: Survey and Perspectivecited by 10
  32. 2023Yes but.. Can ChatGPT Identify Entities in Historical Documents?cited by 10
  33. 2024How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responsescited by 10
  34. 2025PBa-LLM: Privacy- and Bias-aware NLP using Named-Entity Recognition (NER)cited by 10
  35. 2023In the Name of Fairness: Assessing the Bias in Clinical Record De-identificationcited by 9
  36. 2023Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognitioncited by 9
  37. 2023On the Evolution of Knowledge Graphs: A Survey and Perspectivecited by 9
  38. 2024Information Extraction from Clinical Notes: Are We Ready to Switch to Large Language Models?cited by 9

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