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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 41–50 of 63 papers

TitleStatusHype
FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual FrameworkCode0
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity RecognitionCode0
Designing Informative Metrics for Few-Shot Example Selection—0
Enhancing Few-shot NER with Prompt Ordering based Data Augmentation—0
NSP-NER: A Prompt-based Learner for Few-shot NER Driven by Next Sentence Prediction—0
VicunaNER: Zero/Few-shot Named Entity Recognition using Vicuna—0
Few-Shot Named Entity Recognition with Biaffine Span Representation—0
Few-shot Named Entity Recognition with Joint Token and Sentence Awareness—0
Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition—0
Template-free Prompt Tuning for Few-shot NER—0
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