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Few-shot NER

Few-Shot Named Entity Recognition (NER) is the task of recognising a 'named entity' like a person, organization, time and so on in a piece of text e.g. "Alan Mathison [person] visited the Turing Institute [organization] in June [time].

Papers

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Showing 21–30 of 63 papers

TitleStatusHype
Simple Questions Generate Named Entity Recognition DatasetsCode1
Template-free Prompt Tuning for Few-shot NERCode1
An Enhanced Span-based Decomposition Method for Few-Shot Sequence LabelingCode1
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive LearningCode1
Template-Based Named Entity Recognition Using BARTCode1
Few-NERD: A Few-Shot Named Entity Recognition DatasetCode1
Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor LearningCode1
FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual FrameworkCode0
Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace SelectionCode0
CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognition—0
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