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Low Resource Named Entity Recognition
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Low resource named entity recognition is the task of using data and models available for one language for which ample such resources are available (e.g., English) to solve named entity recognition tasks in another, commonly more low-resource, language.
논문
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Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?
GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
RoPDA: Robust Prompt-based Data Augmentation for Low-Resource Named Entity Recognition
Translation and Fusion Improves Zero-shot Cross-lingual Information Extraction
AUC Maximization for Low-Resource Named Entity Recognition
Prompt-based Text Entailment for Low-Resource Named Entity Recognition
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition
SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition
Using Domain Knowledge for Low Resource Named Entity Recognition
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER
Low-Resource Named Entity Recognition Based on Multi-hop Dependency Trigger
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction
Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation
Memorisation versus Generalisation in Pre-trained Language Models
ANEA: Distant Supervision for Low-Resource Named Entity Recognition
A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition
Building Low-Resource NER Models Using Non-Speaker Annotation
Soft Gazetteers for Low-Resource Named Entity Recognition
Distant Supervision and Noisy Label Learning for Low Resource Named Entity Recognition: A Study on Hausa and Yorùbá
Zero-Resource Cross-Lingual Named Entity Recognition
Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models
Towards Robust Named Entity Recognition for Historic German
Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition
Massively Multilingual Transfer for NER
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