Low Resource Named Entity Recognition
15 papers with code • 3 benchmarks • 5 datasets
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.
Benchmarks
These leaderboards are used to track progress in Low Resource Named Entity Recognition
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CoNLL 2003
Few-NERD
Most implemented papers
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER
Recently, prompt-based methods have achieved significant performance in few-shot learning scenarios by bridging the gap between language model pre-training and fine-tuning for downstream tasks.
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition
dfki-nlp/fewie
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RepL4NLP (ACL) 2022
Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-domain data or fine-tuning on in-domain data.
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition
unveiled-the-red-hat/SEE-Few
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COLING 2022
Current few-shot NER methods focus on leveraging existing datasets in the rich-resource domains which might fail in a training-from-scratch setting where no source-domain data is used.
Better Low-Resource Entity Recognition Through Translation and Annotation Fusion
Pre-trained multilingual language models have enabled significant advancements in cross-lingual transfer.
GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
In this paper, we propose GoLLIE (Guideline-following Large Language Model for IE), a model able to improve zero-shot results on unseen IE tasks by virtue of being fine-tuned to comply with annotation guidelines.
