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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 11–20 of 63 papers

TitleStatusHype
Language Model Pre-Training with Sparse Latent TypingCode1
HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity RecognitionCode1
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity RecognitionCode1
An Enhanced Span-based Decomposition Method for Few-Shot Sequence LabelingCode1
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity RecognitionCode1
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NERCode1
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NERCode1
FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity RecognitionCode1
Few-NERD: A Few-Shot Named Entity Recognition DatasetCode1
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor SearchCode1
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