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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
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive LearningCode1
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor SearchCode1
From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot ContextsCode1
Few-shot Named Entity Recognition with Self-describing NetworksCode1
Learning In-context Learning for Named Entity RecognitionCode1
Simple Questions Generate Named Entity Recognition DatasetsCode1
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity RecognitionCode1
A Unified Label-Aware Contrastive Learning Framework for Few-Shot Named Entity Recognition—0
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition—0
Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition—0
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