mlsanityData to 14 Oct 2026

Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challenges

arXiv 2309.14084 ↗First posted 25 Sep 2023cs.CL cs.AI cs.IRcited by 6

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Abstract (arXiv)

In the domain of Natural Language Processing (NLP), Named Entity Recognition (NER) stands out as a pivotal mechanism for extracting structured insights from unstructured text. This manuscript offers an exhaustive exploration into the evolving landscape of NER methodologies, blending foundational principles with contemporary AI advancements. Beginning with the rudimentary concepts of NER, the study spans a spectrum of techniques from traditional rule-based strategies to the contemporary marvels of transformer architectures, particularly highlighting integrations such as BERT with LSTM and CNN. The narrative accentuates domain-specific NER models, tailored for intricate areas like finance, legal, and healthcare, emphasizing their specialized adaptability. Additionally, the research delves into cutting-edge paradigms including reinforcement learning, innovative constructs like E-NER, and the interplay of Optical Character Recognition (OCR) in augmenting NER capabilities. Grounding its insights in practical realms, the paper sheds light on the indispensable role of NER in sectors like finance and biomedicine, addressing the unique challenges they present. The conclusion outlines open challenges and avenues, marking this work as a comprehensive guide for those delving into NER research and applications.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 25 Sep 2023.

Builds on

24 of 35 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  6. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  7. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  8. 2016Neural Architectures for Named Entity Recognitioncited by 550
  9. 2018A Survey on Deep Learning for Named Entity Recognitioncited by 282
  10. 2020Pretrained Transformers for Text Ranking: BERT and Beyondcited by 267
  11. 2023PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Financecited by 222
  12. 2015Named Entity Recognition with Bidirectional LSTM-CNNscited by 201
12 more
  1. 2018Dissecting Contextual Word Embeddings: Architecture and Representationcited by 146
  2. 2018Few-shot classification in Named Entity Recognition Taskcited by 64
  3. 2021Document AI: Benchmarks, Models and Applicationscited by 62
  4. 2023An Empirical Study on Information Extraction using Large Language Modelscited by 46
  5. 2023PromptNER: Prompting For Named Entity Recognitioncited by 35
  6. 2019A BERT Baseline for the Natural Questionscited by 33
  7. 2021ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documentscited by 29
  8. 2019Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Servicecited by 10
  9. 2023E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognitioncited by 7
  10. 2023PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Searchcited by 5
  11. 2023Gaussian Prior Reinforcement Learning for Nested Named Entity Recognitioncited by 3
  12. 2023Improving Domain-Specific Retrieval by NLI Fine-Tuningcited by 1

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Citing papers per year: 2024: 4; 2025: 2.

6 papers.

  1. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 90
  2. 2024Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategiescited by 6
  3. 2024HistNERo: Historical Named Entity Recognition for the Romanian Languagecited by 5
  4. 2024AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMscited by 4
  5. 2025Pre-trained Language Models and Few-shot Learning for Medical Entity Extractioncited by 4
  6. 2025FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual Frameworkcited by 1