mlsanityData to 7 Oct 2026

Deep Active Learning for Named Entity Recognition

arXiv 1707.05928 ↗First posted 19 Jul 2017cs.CLcited by 108

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Abstract (arXiv)

Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning. While active learning is sample-efficient, it can be computationally expensive since it requires iterative retraining. To speed this up, we introduce a lightweight architecture for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and word encoders and a long short term memory (LSTM) tag decoder. The model achieves nearly state-of-the-art performance on standard datasets for the task while being computationally much more efficient than best performing models. We carry out incremental active learning, during the training process, and are able to nearly match state-of-the-art performance with just 25% of the original training data.

Key facts. Cited by 108 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jul 2017.

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Citing papers per year: 2021: 33; 2022: 19; 2023: 30; 2024: 16; 2025: 6; 2026: 4.

108 papers.

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  1. 2021Deep Learning on a Data Diet: Finding Important Examples Early in Trainingcited by 529
  2. 2021Active Learning by Acquiring Contrastive Examplescited by 154
  3. 2022A Comparative Survey of Deep Active Learningcited by 88
  4. 2021On Statistical Bias In Active Learning: How and When To Fix Itcited by 72
  5. 2023Large-scale Dataset Pruning with Dynamic Uncertaintycited by 63
  6. 2021Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answeringcited by 62
  7. 2023Prediction-Oriented Bayesian Active Learningcited by 60
  8. 2021Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentationcited by 56
  9. 2024A Survey of Uncertainty Estimation in LLMs: Theory Meets Practicecited by 54
  10. 2023Active Learning Principles for In-Context Learning with Large Language Modelscited by 53
  11. 2023Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Taskscited by 53
  12. 2021A Survey on Green Deep Learningcited by 52
38 more
  1. 2022A Survey of Active Learning for Natural Language Processingcited by 40
  2. 2021Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimatescited by 39
  3. 2021Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approachcited by 34
  4. 2021LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentationcited by 33
  5. 2021On the Importance of Effectively Adapting Pretrained Language Models for Active Learningcited by 31
  6. 2021Improving Contrastive Learning on Imbalanced Seed Data via Open-World Samplingcited by 30
  7. 2021Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximizationcited by 28
  8. 2021Cartography Active Learningcited by 26
  9. 2021Uncertainty-Aware Reliable Text Classificationcited by 23
  10. 2021A Tale Of Two Long Tailscited by 19
  11. 2022Multi-task Active Learning for Pre-trained Transformer-based Modelscited by 16
  12. 2022PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learningcited by 14
  13. 2023Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Taskscited by 13
  14. 2022Towards Computationally Feasible Deep Active Learningcited by 11
  15. 2022Is margin all you need? An extensive empirical study of active learning on tabular datacited by 10
  16. 2023Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecturecited by 10
  17. 2021Diversity-Aware Batch Active Learning for Dependency Parsingcited by 9
  18. 2023On the Limitations of Simulating Active Learningcited by 9
  19. 2021LMTurk: Few-Shot Learners as Crowdsourcing Workers in a Language-Model-as-a-Service Frameworkcited by 8
  20. 2022FAMIE: A Fast Active Learning Framework for Multilingual Information Extractioncited by 8
  21. 2023Agile Modeling: From Concept to Classifier in Minutescited by 8
  22. 2022Deep Active Learning for Computer Vision: Past and Futurecited by 7
  23. 2021Active2 Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translationcited by 6
  24. 2021Improving Question Answering Performance Using Knowledge Distillation and Active Learningcited by 6
  25. 2021Single-Modal Entropy based Active Learning for Visual Question Answeringcited by 6
  26. 2022Eeny, meeny, miny, moe. How to choose data for morphological inflectioncited by 6
  27. 2023Active Learning for Abstractive Text Summarizationcited by 6
  28. 2023Investigating Multi-source Active Learning for Natural Language Inferencecited by 6
  29. 2021Mitigating Sampling Bias and Improving Robustness in Active Learningcited by 5
  30. 2021Bayesian Active Summarizationcited by 5
  31. 2022Efficient Argument Structure Extraction with Transfer Learning and Active Learningcited by 5
  32. 2024STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Modelscited by 5
  33. 2024Hallucination Diversity-Aware Active Learning for Text Summarizationcited by 5
  34. 2021LaTeX-Numeric: Language-agnostic Text attribute eXtraction for E-commerce Numeric Attributescited by 4
  35. 2023A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Predictioncited by 4
  36. 2023Re-weighting Tokens: A Simple and Effective Active Learning Strategy for Named Entity Recognitioncited by 4
  37. 2022On Efficiently Acquiring Annotations for Multilingual Modelscited by 3
  38. 2022Deep reinforced active learning for multi-class image classificationcited by 3

50 most cited of 108 All 108 (JSON)