mlsanityData to 7 Oct 2026

Simple Questions Generate Named Entity Recognition Datasets

arXiv 2112.08808 ↗First posted 16 Dec 2021cs.CLcited by 5

Not on the map. Why?

Abstract (arXiv)

Recent named entity recognition (NER) models often rely on human-annotated datasets, requiring the significant engagement of professional knowledge on the target domain and entities. This research introduces an ask-to-generate approach that automatically generates NER datasets by asking questions in simple natural language to an open-domain question answering system (e.g., "Which disease?"). Despite using fewer in-domain resources, our models, solely trained on the generated datasets, largely outperform strong low-resource models by an average F1 score of 19.4 for six popular NER benchmarks. Furthermore, our models provide competitive performance with rich-resource models that additionally leverage in-domain dictionaries provided by domain experts. In few-shot NER, we outperform the previous best model by an F1 score of 5.2 on three benchmarks and achieve new state-of-the-art performance.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 24 of its 50 references that are in the map. First posted on arXiv 16 Dec 2021.

Builds on

24 of 50 references.

References · 24

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  4. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
  5. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
  6. 2017Reading Wikipedia to Answer Open-Domain Questionscited by 777
  7. 2019Latent Retrieval for Weakly Supervised Open Domain Question Answeringcited by 691
  8. 2016Neural Architectures for Named Entity Recognitioncited by 548
  9. 2019A Unified MRC Framework for Named Entity Recognitioncited by 199
  10. 2021Template-Based Named Entity Recognition Using BARTcited by 153
  11. 2020BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervisioncited by 91
  12. 2020Learning Dense Representations of Phrases at Scalecited by 91
12 more
  1. 2017Automated Phrase Mining from Massive Text Corporacited by 70
  2. 2018Learning Named Entity Tagger using Domain-Specific Dictionarycited by 65
  3. 2019Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Indexcited by 64
  4. 2020Few-Shot Named Entity Recognition: A Comprehensive Studycited by 41
  5. 2021Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Trainingcited by 41
  6. 2021R2-D2: A Modular Baseline for Open-Domain Question Answeringcited by 37
  7. 2021Phrase Retrieval Learns Passage Retrieval, Toocited by 33
  8. 2019Low-Resource Name Tagging Learned with Weakly Labeled Datacited by 32
  9. 2021Noisy-Labeled NER with Confidence Estimationcited by 28
  10. 2021Weakly Supervised Named Entity Tagging with Learnable Logical Rulescited by 21
  11. 2021Question Answering Infused Pre-training of General-Purpose Contextualized Representationscited by 19
  12. 2021Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Frameworkcited by 15

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 2.

5 papers.

  1. 2022SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognitioncited by 4
  2. 2023Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learningcited by 2
  3. 2022Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representationscited by 1
  4. 2024ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Modelscited by 1
  5. 2024Revealing Trends in Datasets from the 2022 ACL and EMNLP Conferencescited by 0