Data to 14 Oct 2026

Few-shot classification in Named Entity Recognition Task

arXiv 1812.06158 ↗First posted 14 Dec 2018cs.CL cs.LG stat.MLcited by 64

Not on the map. Why?

Abstract (arXiv)

For many natural language processing (NLP) tasks the amount of annotated data is limited. This urges a need to apply semi-supervised learning techniques, such as transfer learning or meta-learning. In this work we tackle Named Entity Recognition (NER) task using Prototypical Network - a metric learning technique. It learns intermediate representations of words which cluster well into named entity classes. This property of the model allows classifying words with extremely limited number of training examples, and can potentially be used as a zero-shot learning method. By coupling this technique with transfer learning we achieve well-performing classifiers trained on only 20 instances of a target class.

Key facts. Cited by 64 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Dec 2018.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 18; 2022: 14; 2023: 20; 2024: 10; 2025: 2.

64 papers.

Sort:

2021Few-NERD: A Few-Shot Named Entity Recognition Datasetcited by 152
2021Template-free Prompt Tuning for Few-shot NERcited by 73
2021CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learningcited by 64
2021Named Entity Recognition and Classification on Historical Documents: A Surveycited by 47
2021Few-shot Intent Classification and Slot Filling with Retrieved Examplescited by 35
2021Controllable Semantic Parsing via Retrieval Augmentationcited by 35
2023Zero-shot cross-lingual transfer language selection using linguistic similaritycited by 28
2021LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Promptingcited by 27
2021An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labelingcited by 26
2023NBIAS: A Natural Language Processing Framework for Bias Identification in Textcited by 23
2022Decomposed Meta-Learning for Few-Shot Named Entity Recognitioncited by 22
2021Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognitioncited by 21

38 more

2022Label Semantics for Few Shot Named Entity Recognitioncited by 21
2022Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypescited by 17
2022Few-shot Named Entity Recognition with Self-describing Networkscited by 16
2021Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Frameworkcited by 15
2023How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domaincited by 14
2021Linguistically-Enriched and Context-Aware Zero-shot Slot Fillingcited by 12
2022Prompt-Based Metric Learning for Few-Shot NERcited by 12
2024Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model promptingcited by 12
2024LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognitioncited by 11
2021What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Datacited by 10
2021Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networkscited by 10
2023A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NERcited by 9
2022SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognitioncited by 8
2023Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learningcited by 8
2023Learning In-context Learning for Named Entity Recognitioncited by 7
2024Argument Mining in Data Scarce Settings: Cross-lingual Transfer and Few-shot Techniquescited by 7
2023Comprehensive Overview of Named Entity Recognition: Models, Domain-Specific Applications and Challengescited by 6
2022Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Taggingcited by 5
2023Few-shot Event Detection: An Empirical Study and a Unified Viewcited by 5
2023PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Searchcited by 5
2021MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Taggingcited by 4
2022On the cross-lingual transferability of multilingual prototypical models across NLU taskscited by 4
2022SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognitioncited by 4
2023Type-Aware Decomposed Framework for Few-Shot Named Entity Recognitioncited by 4
2023W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognitioncited by 4
2023MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognitioncited by 4
2023A Survey on Semantic Processing Techniquescited by 4
2023Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purificationcited by 4
2021Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language Modelscited by 3
2022Event Extraction: A Surveycited by 3
2023ContrastNER: Contrastive-based Prompt Tuning for Few-shot NERcited by 3
2024Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognitioncited by 3
2024BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognitioncited by 3
2022TOKEN is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Modelscited by 2
2023Toward Open-domain Slot Filling via Self-supervised Co-trainingcited by 2
2023From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contextscited by 2
2024Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognitioncited by 2
2021Few-shot Named Entity Recognition with Cloze Questionscited by 1

50 most cited of 64 All 64 (JSON)