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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 51–63 of 63 papers

TitleStatusHype
A Model Ensemble Approach with LLM for Chinese Text ClassificationCode0
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text ReconstructionCode0
FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual FrameworkCode0
TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity RecognitionCode0
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity RecognitionCode0
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation PurificationCode0
PCBERT: Parent and Child BERT for Chinese Few-shot NERCode0
Learning from Miscellaneous Other-Class Words for Few-shot Named Entity RecognitionCode0
Robustness of Demonstration-based Learning Under Limited Data ScenarioCode0
Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace SelectionCode0
Decomposed Meta-Learning for Few-Shot Sequence LabelingCode0
LLMs in Biomedicine: A study on clinical Named Entity RecognitionCode0
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and RecognitionCode0
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