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An Analysis of Simple Data Augmentation for Named Entity Recognition

arXiv 2010.11683 ↗First posted 22 Oct 2020cs.CLcited by 75

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Abstract (arXiv)

Simple yet effective data augmentation techniques have been proposed for sentence-level and sentence-pair natural language processing tasks. Inspired by these efforts, we design and compare data augmentation for named entity recognition, which is usually modeled as a token-level sequence labeling problem. Through experiments on two data sets from the biomedical and materials science domains (i2b2-2010 and MaSciP), we show that simple augmentation can boost performance for both recurrent and transformer-based models, especially for small training sets.

Key facts. Cited by 75 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 16; 2022: 23; 2023: 16; 2024: 11; 2025: 4; 2026: 5.

75 papers.

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  1. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  2. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  3. 2021NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentationcited by 72
  4. 2022PromDA: Prompt-based Data Augmentation for Low-Resource NLU Taskscited by 51
  5. 2021Named Entity Recognition and Classification on Historical Documents: A Surveycited by 47
  6. 2021MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NERcited by 42
  7. 2021Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Trainingcited by 41
  8. 2021Gradient Imitation Reinforcement Learning for Low Resource Relation Extractioncited by 33
  9. 2022Wojood: Nested Arabic Named Entity Corpus and Recognition using BERTcited by 32
  10. 2022LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Taggingcited by 29
  11. 2024Prompt Perturbation Consistency Learning for Robust Language Modelscited by 26
  12. 2021Substructure Substitution: Structured Data Augmentation for NLPcited by 25
38 more
  1. 2021Context-aware Adversarial Training for Name Regularity Bias in Named Entity Recognitioncited by 19
  2. 2022MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspectivecited by 17
  3. 2021Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systemscited by 16
  4. 2022TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understandingcited by 15
  5. 2022Intriguing Properties of Compression on Multilingual Modelscited by 15
  6. 2021Data Augmentation for Cross-Domain Named Entity Recognitioncited by 14
  7. 2023Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extractioncited by 12
  8. 2024LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertaintycited by 12
  9. 2022GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentationcited by 11
  10. 2024LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognitioncited by 11
  11. 2021Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslationcited by 10
  12. 2023ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NERcited by 9
  13. 2024CoDa: Constrained Generation based Data Augmentation for Low-Resource NLPcited by 9
  14. 2023A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trendscited by 8
  15. 2023Data Augmentation for Low-Resource Keyphrase Generationcited by 8
  16. 2024ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptionscited by 8
  17. 2022FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognitioncited by 7
  18. 2022PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Taskscited by 7
  19. 2023E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognitioncited by 7
  20. 2022Improved Text Classification via Test-Time Augmentationcited by 6
  21. 2022Entity-to-Text based Data Augmentation for various Named Entity Recognition Taskscited by 6
  22. 2023BioAug: Conditional Generation based Data Augmentation for Low-Resource Biomedical NERcited by 5
  23. 2022Linear programming word problems formulation using EnsembleCRF NER labeler and T5 text generator with data augmentationscited by 4
  24. 2022Robust Self-Augmentation for Named Entity Recognition with Meta Reweightingcited by 3
  25. 2022Named Entity Recognition in Industrial Tables using Tabular Language Modelscited by 3
  26. 2022Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classificationcited by 3
  27. 2023Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documentscited by 3
  28. 2023Boosting Event Extraction with Denoised Structure-to-Text Augmentationcited by 3
  29. 2024Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?cited by 3
  30. 2024Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategycited by 3
  31. 2021Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children's mindreading abilitycited by 2
  32. 2021NATURE: Natural Auxiliary Text Utterances for Realistic Spoken Language Evaluationcited by 2
  33. 2022Using Synthetic Data for Conversational Response Generation in Low-resource Settingscited by 2
  34. 2022Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Taskcited by 2
  35. 2022Style Transfer as Data Augmentation: A Case Study on Named Entity Recognitioncited by 2
  36. 2023Mitigating Data Scarcity for Large Language Modelscited by 2
  37. 2024An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domainscited by 2
  38. 2021Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articlescited by 1

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