mlsanityData to 14 Oct 2026

Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework

arXiv 2109.05357 ↗First posted 11 Sep 2021cs.CLcited by 15

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Abstract (arXiv)

In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings. Built upon large-scale pre-trained language models, we propose a novel NER framework, namely SpanNER, which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. We perform extensive experiments on 5 benchmark datasets and evaluate the proposed method in the few-shot learning, domain transfer and zero-shot learning settings. The experimental results show that the proposed method can bring 10%, 23% and 26% improvements in average over the best baselines in few-shot learning, domain transfer and zero-shot learning settings respectively.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 21 of its 39 references that are in the map. First posted on arXiv 11 Sep 2021.

Builds on

21 of 39 references.

References · 21

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  9. 2019A Unified MRC Framework for Named Entity Recognitioncited by 199
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9 more
  1. 2020SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teachingcited by 71
  2. 2018Few-shot classification in Named Entity Recognition Taskcited by 64
  3. 2020Few-Shot Named Entity Recognition: A Comprehensive Studycited by 41
  4. 2019Label-Agnostic Sequence Labeling by Copying Nearest Neighborscited by 27
  5. 2018Few-shot Learning for Named Entity Recognition in Medical Textcited by 19
  6. 2019Zero-Shot Open Entity Typing as Type-Compatible Groundingcited by 19
  7. 2020Example-Based Named Entity Recognitioncited by 19
  8. 2020Adaptive Self-training for Few-shot Neural Sequence Labelingcited by 18
  9. 2018Zero-shot Sequence Labeling: Transferring Knowledge from Sentences to Tokenscited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 4; 2024: 5; 2025: 0; 2026: 1.

15 papers.

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  1. 2022Decomposed Meta-Learning for Few-Shot Named Entity Recognitioncited by 22
  2. 2022Few-shot Named Entity Recognition with Self-describing Networkscited by 16
  3. 2021LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learnerscited by 9
  4. 2023Learning In-context Learning for Named Entity Recognitioncited by 7
  5. 2021Simple Questions Generate Named Entity Recognition Datasetscited by 5
  6. 2023Type-Aware Decomposed Framework for Few-Shot Named Entity Recognitioncited by 4
  7. 2024BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognitioncited by 3
  8. 2022Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognitioncited by 1
  9. 2023Unifying Token and Span Level Supervisions for Few-Shot Sequence Labelingcited by 1
  10. 2024Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstructioncited by 1
  11. 2024ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Modelscited by 1
  12. 2024Few-shot Named Entity Recognition via Superposition Concept Discriminationcited by 1
3 more
  1. 2023Causal Interventions-based Few-Shot Named Entity Recognitioncited by 0
  2. 2024Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognitioncited by 0
  3. 2026Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approachcited by 0