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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 1–10 of 63 papers

TitleStatusHype
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
Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition—0
llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models—0
A Unified Label-Aware Contrastive Learning Framework for Few-Shot Named Entity Recognition—0
LLMs in Biomedicine: A study on clinical Named Entity RecognitionCode0
Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning—0
Few-shot Named Entity Recognition via Superposition Concept DiscriminationCode0
A Model Ensemble Approach with LLM for Chinese Text ClassificationCode0
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