mlsanityData to 14 Oct 2026

Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition

arXiv 2106.15167 ↗First posted 29 Jun 2021cs.CLcited by 21

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Abstract (arXiv)

Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to identify and classify named entity mentions. Prototypical network shows superior performance on few-shot NER. However, existing prototypical methods fail to differentiate rich semantics in other-class words, which will aggravate overfitting under few shot scenario. To address the issue, we propose a novel model, Mining Undefined Classes from Other-class (MUCO), that can automatically induce different undefined classes from the other class to improve few-shot NER. With these extra-labeled undefined classes, our method will improve the discriminative ability of NER classifier and enhance the understanding of predefined classes with stand-by semantic knowledge. Experimental results demonstrate that our model outperforms five state-of-the-art models in both 1-shot and 5-shots settings on four NER benchmarks. We will release the code upon acceptance. The source code is released on https: //github.com/shuaiwa16/OtherClassNER.git.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 19 of its 40 references that are in the map. First posted on arXiv 29 Jun 2021.

Builds on

19 of 40 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  4. 2016Neural Architectures for Named Entity Recognitioncited by 550
  5. 2018Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfacescited by 377
  6. 2017NormFace: L2 Hypersphere Embedding for Face Verificationcited by 237
  7. 2019Dice Loss for Data-imbalanced NLP Taskscited by 182
  8. 2019A Survey on Recent Advances in Named Entity Recognition from Deep Learning modelscited by 117
  9. 2017Joint Extraction of Entities and Relations Based on a Novel Tagging Schemecited by 102
  10. 2020Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Networkcited by 83
  11. 2017Zero-Shot Transfer Learning for Event Extractioncited by 66
  12. 2018Few-shot classification in Named Entity Recognition Taskcited by 64
7 more
  1. 2019Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detectioncited by 54
  2. 2019Low-Resource Name Tagging Learned with Weakly Labeled Datacited by 32
  3. 2017SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Datacited by 28
  4. 2017Train Once, Test Anywhere: Zero-Shot Learning for Text Classificationcited by 22
  5. 2020CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinesecited by 19
  6. 2017Improving One-Shot Learning through Fusing Side Informationcited by 10
  7. 2018Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognitioncited by 9

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Citing papers per year: 2021: 1; 2022: 9; 2023: 9; 2024: 2.

21 papers.

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  1. 2021Template-free Prompt Tuning for Few-shot NERcited by 73
  2. 2022Decomposed Meta-Learning for Few-Shot Named Entity Recognitioncited by 22
  3. 2023Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuningcited by 21
  4. 2022Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypescited by 17
  5. 2022Few-shot Named Entity Recognition with Self-describing Networkscited by 16
  6. 2022Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NERcited by 8
  7. 2023Learning In-context Learning for Named Entity Recognitioncited by 7
  8. 2022Embedding Hallucination for Few-Shot Language Fine-tuningcited by 5
  9. 2023PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Searchcited by 5
  10. 2024Cross-domain Named Entity Recognition via Graph Matchingcited by 5
  11. 2022SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognitioncited by 4
  12. 2023MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognitioncited by 4
9 more
  1. 2023ProtoNER: Few shot Incremental Learning for Named Entity Recognition using Prototypical Networkscited by 2
  2. 2022Prompt-based Text Entailment for Low-Resource Named Entity Recognitioncited by 1
  3. 2022Reconciliation of Pre-trained Models and Prototypical Neural Networks in Few-shot Named Entity Recognitioncited by 1
  4. 2023Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognitioncited by 1
  5. 2023Unifying Token and Span Level Supervisions for Few-Shot Sequence Labelingcited by 1
  6. 2024Few-shot Named Entity Recognition via Superposition Concept Discriminationcited by 1
  7. 2022A Sequence Tagging based Framework for Few-Shot Relation Extractioncited by 0
  8. 2023Causal Interventions-based Few-Shot Named Entity Recognitioncited by 0
  9. 2023A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methodscited by 0