mlsanityData to 14 Oct 2026

Label Semantics for Few Shot Named Entity Recognition

arXiv 2203.08985 ↗First posted 16 Mar 2022cs.CLcited by 21

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Abstract (arXiv)

We study the problem of few shot learning for named entity recognition. Specifically, we leverage the semantic information in the names of the labels as a way of giving the model additional signal and enriched priors. We propose a neural architecture that consists of two BERT encoders, one to encode the document and its tokens and another one to encode each of the labels in natural language format. Our model learns to match the representations of named entities computed by the first encoder with label representations computed by the second encoder. The label semantics signal is shown to support improved state-of-the-art results in multiple few shot NER benchmarks and on-par performance in standard benchmarks. Our model is especially effective in low resource settings.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 26 of its 44 references that are in the map. First posted on arXiv 16 Mar 2022.

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26 of 44 references.

References · 26

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
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  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  5. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  6. 2016Matching Networks for One Shot Learningcited by 2,255
  7. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,143
  8. 2016Neural Architectures for Named Entity Recognitioncited by 550
  9. 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 546
  10. 2018FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluationcited by 254
  11. 2021Muppet: Massive Multi-task Representations with Pre-Finetuningcited by 192
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14 more
  1. 2021Template-Based Named Entity Recognition Using BARTcited by 153
  2. 2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 152
  3. 2019Zero-Shot Entity Linking by Reading Entity Descriptionscited by 124
  4. 2020Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Networkcited by 83
  5. 2018Few-shot classification in Named Entity Recognition Taskcited by 64
  6. 2018Diverse Few-Shot Text Classification with Multiple Metricscited by 61
  7. 2019Few-shot Text Classification with Distributional Signaturescited by 58
  8. 2020Named Entity Recognition without Labelled Data: A Weak Supervision Approachcited by 46
  9. 2020Few-Shot Named Entity Recognition: A Comprehensive Studycited by 41
  10. 2020Local Additivity Based Data Augmentation for Semi-supervised NERcited by 31
  11. 2020Augmented Natural Language for Generative Sequence Labelingcited by 29
  12. 2019Zero-Shot Open Entity Typing as Type-Compatible Groundingcited by 19
  13. 2021Personalized Embedding-based e-Commerce Recommendations at eBaycited by 12
  14. 2020Linking Entities to Unseen Knowledge Bases with Arbitrary Schemascited by 7

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Citing papers per year: 2022: 5; 2023: 14; 2024: 1; 2025: 1.

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  1. 2023Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!cited by 119
  2. 2022Label Semantic Aware Pre-training for Few-shot Text Classificationcited by 18
  3. 2022Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysiscited by 16
  4. 2022Prompt-Based Metric Learning for Few-Shot NERcited by 12
  5. 2023A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NERcited by 9
  6. 2022SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognitioncited by 8
  7. 2023Ultra-Fine Entity Typing with Prior Knowledge about Labels: A Simple Clustering Based Strategycited by 8
  8. 2023UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspectivecited by 7
  9. 2023Learning In-context Learning for Named Entity Recognitioncited by 7
  10. 2023Few-shot Event Detection: An Empirical Study and a Unified Viewcited by 5
  11. 2023PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Searchcited by 5
  12. 2023DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisalcited by 5
9 more
  1. 2023Type-Aware Decomposed Framework for Few-Shot Named Entity Recognitioncited by 4
  2. 2023Pre-Finetuning for Few-Shot Emotional Speech Recognitioncited by 4
  3. 2022Contrastive Training Improves Zero-Shot Classification of Semi-structured Documentscited by 3
  4. 2023ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognitioncited by 2
  5. 2023TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognitioncited by 2
  6. 2023Unifying Token and Span Level Supervisions for Few-Shot Sequence Labelingcited by 1
  7. 2023Taxonomy Expansion for Named Entity Recognitioncited by 0
  8. 2024A Unified Label-Aware Contrastive Learning Framework for Few-Shot Named Entity Recognitioncited by 0
  9. 2025Adversarial Demonstration Learning for Low-resource NER Using Dual Similaritycited by 0