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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
mGPT: Few-Shot Learners Go MultilingualCode2
Decomposed Meta-Learning for Few-Shot Named Entity RecognitionCode2
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated DataCode1
HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity RecognitionCode1
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NERCode1
How far is Language Model from 100% Few-shot Named Entity Recognition in Medical DomainCode1
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor SearchCode1
Learning In-context Learning for Named Entity RecognitionCode1
From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot ContextsCode1
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity RecognitionCode1
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