mlsanityData to 14 Oct 2026

Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks

arXiv 2210.10343 ↗First posted 19 Oct 2022cs.CL cs.AIcited by 6

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Abstract (arXiv)

Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks). Existing augmentation techniques either manipulate the words in the original text that break the semantic coherence of the text, or exploit generative models that ignore preserving entities in the original text, which impedes the use of augmentation techniques on nested and discontinuous NER tasks. In this work, we propose a novel Entity-to-Text based data augmentation technique named EnTDA to add, delete, replace or swap entities in the entity list of the original texts, and adopt these augmented entity lists to generate semantically coherent and entity preserving texts for various NER tasks. Furthermore, we introduce a diversity beam search to increase the diversity during the text generation process. Experiments on thirteen NER datasets across three tasks (flat, nested, and discontinuous NER tasks) and two settings (full data and low resource settings) show that EnTDA could bring more performance improvements compared to the baseline augmentation techniques.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 34 of its 57 references that are in the map. First posted on arXiv 19 Oct 2022.

Builds on

34 of 57 references.

References · 34

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  11. 2019Not Enough Data? Deep Learning to the Rescue!cited by 175
  12. 2020Data Augmentation using Pre-trained Transformer Modelscited by 168
22 more
  1. 2018QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehensioncited by 164
  2. 2020Named Entity Recognition as Dependency Parsingcited by 151
  3. 2021A Unified Generative Framework for Various NER Subtaskscited by 141
  4. 2016Sequence-to-Sequence Learning as Beam-Search Optimizationcited by 127
  5. 2020CrossNER: Evaluating Cross-Domain Named Entity Recognitioncited by 104
  6. 2021Unified Named Entity Recognition as Word-Word Relation Classificationcited by 90
  7. 2020Syntactic Data Augmentation Increases Robustness to Inference Heuristicscited by 86
  8. 2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
  9. 2020An Analysis of Simple Data Augmentation for Named Entity Recognitioncited by 76
  10. 2018Sequence-to-Sequence Data Augmentation for Dialogue Language Understandingcited by 68
  11. 2019Generalized Data Augmentation for Low-Resource Translationcited by 53
  12. 2019Data Augmentation via Dependency Tree Morphing for Low-Resource Languagescited by 45
  13. 2020SelfORE: Self-supervised Relational Feature Learning for Open Relation Extractioncited by 43
  14. 2021MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NERcited by 42
  15. 2020Semi-supervised Relation Extraction via Incremental Meta Self-Trainingcited by 35
  16. 2020Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweightcited by 34
  17. 2021A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognitioncited by 33
  18. 2021Gradient Imitation Reinforcement Learning for Low Resource Relation Extractioncited by 33
  19. 2022HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extractioncited by 32
  20. 2022Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graphcited by 22
  21. 2022Pair-Level Supervised Contrastive Learning for Natural Language Inferencecited by 12
  22. 2022A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inferencecited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 2; 2026: 1.

6 papers.

  1. 2023Large Language Models for Generative Information Extraction: A Surveycited by 185
  2. 2024ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptionscited by 8
  3. 2023Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extractioncited by 5
  4. 2025Concept Extraction for Time Series with ECLAD-tscited by 2
  5. 2025SEDA: A Self-Adapted Entity-Centric Data Augmentation for Boosting Gird-based Discontinuous NER Modelscited by 0
  6. 2026ToxiTrace: Gradient-Aligned Training for Explainable Chinese Toxicity Detectioncited by 0