SOTAVerified

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 31–40 of 63 papers

TitleStatusHype
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition—0
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity RecognitionCode1
Type-Aware Decomposed Framework for Few-Shot Named Entity RecognitionCode1
Prompt-Based Metric Learning for Few-Shot NERCode1
Language Model Pre-Training with Sparse Latent TypingCode1
Robustness of Demonstration-based Learning Under Limited Data ScenarioCode0
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition—0
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity RecognitionCode1
PCBERT: Parent and Child BERT for Chinese Few-shot NERCode0
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity RecognitionCode1
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