Low Resource Named Entity Recognition
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Benchmarks
Most implemented
Towards Robust Named Entity Recognition for Historic German
GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition
Papers
Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields
Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems require tens of thousands of annotated sentences in order to obtain high performance. However, for most of the world's …
Low Resource Named Entity Recognitionnamed-entity-recognitionNamed Entity RecognitionTransfer LearningLow-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?
Named entity recognition (NER), a task that identifies and categorizes named entities such as persons or organizations from text, is traditionally framed as a multi-class classification problem. However, this approach of…
AllLow Resource Named Entity RecognitionMeta-LearningMulti-class Classification+4GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
Large Language Models (LLMs) combined with instruction tuning have made significant progress when generalizing to unseen tasks. However, they have been less successful in Information Extraction (IE), lagging behind task-…
Event Argument ExtractionEvent ExtractionLanguage ModelingLanguage Modelling+7RoPDA: Robust Prompt-based Data Augmentation for Low-Resource Named Entity Recognition
Data augmentation has been widely used in low-resource NER tasks to tackle the problem of data sparsity. However, previous data augmentation methods have the disadvantages of disrupted syntactic structures, token-label m…
Data AugmentationLow Resource Named Entity Recognitionnamed-entity-recognitionNamed Entity Recognition+1Translation and Fusion Improves Zero-shot Cross-lingual Information Extraction
Large language models (LLMs) combined with instruction tuning have shown significant progress in information extraction (IE) tasks, exhibiting strong generalization capabilities to unseen datasets by following annotation…
Cross-Lingual TransferDecoderLow Resource Named Entity Recognitionnamed-entity-recognition+5AUC Maximization for Low-Resource Named Entity Recognition
Current work in named entity recognition (NER) uses either cross entropy (CE) or conditional random fields (CRF) as the objective/loss functions to optimize the underlying NER model. Both of these traditional objective f…
Low Resource Named Entity Recognitionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+1