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Neural Architectures for Named Entity Recognition

arXiv 1603.01360 ↗First posted 4 Mar 2016cs.CLcited by 548

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Abstract (arXiv)

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available. In this paper, we introduce two new neural architectures---one based on bidirectional LSTMs and conditional random fields, and the other that constructs and labels segments using a transition-based approach inspired by shift-reduce parsers. Our models rely on two sources of information about words: character-based word representations learned from the supervised corpus and unsupervised word representations learned from unannotated corpora. Our models obtain state-of-the-art performance in NER in four languages without resorting to any language-specific knowledge or resources such as gazetteers.

Key facts. Cited by 548 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Mar 2016.

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Citing papers per year: 2021: 165; 2022: 124; 2023: 116; 2024: 66; 2025: 32; 2026: 45.

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  1. 2022LayoutLMv3: Pre-training for Document AI with Unified Text and Image Maskingcited by 481
  2. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 376
  3. 2023Analyzing Leakage of Personally Identifiable Information in Language Modelscited by 278
  4. 2022GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Recordscited by 238
  5. 2022Unified Structure Generation for Universal Information Extractioncited by 220
  6. 2024Security and Privacy Challenges of Large Language Models: A Surveycited by 179
  7. 2023GPT-NER: Named Entity Recognition via Large Language Modelscited by 158
  8. 2021MasakhaNER: Named Entity Recognition for African Languagescited by 155
  9. 2021Template-Based Named Entity Recognition Using BARTcited by 153
  10. 2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 150
  11. 2021A Unified Generative Framework for Various NER Subtaskscited by 141
  12. 2021A Comprehensive Survey of Scene Graphs: Generation and Applicationcited by 125
38 more
  1. 2021Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Basescited by 107
  2. 2022LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understandingcited by 107
  3. 2022The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymizationcited by 94
  4. 2025A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applicationscited by 91
  5. 2021StrucTexT: Structured Text Understanding with Multi-Modal Transformerscited by 90
  6. 2021Unified Named Entity Recognition as Word-Word Relation Classificationcited by 89
  7. 2023On the Opportunities and Challenges of Foundation Models for Geospatial Artificial Intelligencecited by 88
  8. 2023GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformercited by 84
  9. 2022BioBART: Pretraining and Evaluation of A Biomedical Generative Language Modelcited by 83
  10. 2021Improving Named Entity Recognition by External Context Retrieving and Cooperative Learningcited by 76
  11. 2021ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Informationcited by 65
  12. 2021Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solutioncited by 64
  13. 2021CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learningcited by 64
  14. 2021SpanNER: Named Entity Re-/Recognition as Span Predictioncited by 55
  15. 2023Visual Information Extraction in the Wild: Practical Dataset and End-to-end Solutioncited by 54
  16. 2021MAVE: A Product Dataset for Multi-source Attribute Value Extractioncited by 53
  17. 2021Text-to-Table: A New Way of Information Extractioncited by 47
  18. 2021Named Entity Recognition and Classification on Historical Documents: A Surveycited by 47
  19. 2022FiNER: Financial Numeric Entity Recognition for XBRL Taggingcited by 47
  20. 2023Universal Information Extraction as Unified Semantic Matchingcited by 47
  21. 2021Temporal Effects on Pre-trained Models for Language Processing Taskscited by 46
  22. 2022Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completioncited by 44
  23. 2021Spatial Dual-Modality Graph Reasoning for Key Information Extractioncited by 43
  24. 2021MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NERcited by 42
  25. 2021MReD: A Meta-Review Dataset for Structure-Controllable Text Generationcited by 42
  26. 2021Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Trainingcited by 41
  27. 2021A Sequence-to-Set Network for Nested Named Entity Recognitioncited by 40
  28. 2021MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understandingcited by 40
  29. 2021Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimatescited by 39
  30. 2021Paradigm Shift in Natural Language Processingcited by 39
  31. 2021Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NERcited by 38
  32. 2023ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extractioncited by 38
  33. 2021RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NERcited by 37
  34. 2022T-NER: An All-Round Python Library for Transformer-based Named Entity Recognitioncited by 37
  35. 2021Learning Rich Representation of Keyphrases from Textcited by 36
  36. 2023Instruct and Extract: Instruction Tuning for On-Demand Information Extractioncited by 35
  37. 2021Lexicon Enhanced Chinese Sequence Labeling Using BERT Adaptercited by 34
  38. 2021A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognitioncited by 33

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