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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
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition—0
Designing Informative Metrics for Few-Shot Example Selection—0
Decomposed Meta-Learning for Few-Shot Sequence LabelingCode0
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated DataCode1
LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition—0
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text ReconstructionCode0
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation PurificationCode0
HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity RecognitionCode1
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related FeaturesCode0
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NERCode1
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