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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 Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition—0
Robustness of Demonstration-based Learning Under Limited Data ScenarioCode0
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition—0
PCBERT: Parent and Child BERT for Chinese Few-shot NERCode0
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP—0
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity RecognitionCode0
Template-free Prompt Tuning for Few-shot NER—0
Few-Shot Named Entity Recognition with Biaffine Span Representation—0
NSP-NER: A Prompt-based Learner for Few-shot NER Driven by Next Sentence Prediction—0
Few-shot Named Entity Recognition with Joint Token and Sentence Awareness—0
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning—0
Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and RecognitionCode0
Learning from Miscellaneous Other-Class Words for Few-shot Named Entity RecognitionCode0
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