Few-shot NER
4개 벤치마크 · 논문 66편 · 이 태스크의 논문 보기 →
Benchmarks
Most implemented
Few-NERD: A Few-Shot Named Entity Recognition Dataset
Learning In-context Learning for Named Entity Recognition
Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual Framework
Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace Selection
LLMs in Biomedicine: A study on clinical Named Entity Recognition
Papers
PANER: A Paraphrase-Augmented Framework for Low-Resource Named Entity Recognition
Named Entity Recognition (NER) is a critical task that requires substantial annotated data, making it challenging in low-resource scenarios where label acquisition is expensive. While zero-shot and instruction-tuned appr…
Data AugmentationFew-shot NERReProCon: Scalable and Resource-Efficient Few-Shot Biomedical Named Entity Recognition
Named Entity Recognition (NER) in biomedical domains faces challenges due to data scarcity and imbalanced label distributions, especially with fine-grained entity types. We propose ReProCon, a novel few-shot NER framewor…
Contrastive LearningFew-shot NEREnhancing Hindi NER in Low Context: A Comparative study of Transformer-based models with vs. without Retrieval Augmentation
One major challenge in natural language processing is named entity recognition (NER), which identifies and categorises named entities in textual input. In order to improve NER, this study investigates a Hindi NER techniq…
Data AugmentationFew-shot NERFewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual Framework
We introduce FewTopNER, a novel framework that integrates few-shot named entity recognition (NER) with topic-aware contextual modeling to address the challenges of cross-lingual and low-resource scenarios. FewTopNER leve…
Entity DisambiguationFew-Shot Learningfew-shot-nerFew-shot NER+4Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace Selection
Named-entity recognition (NER) is a task that typically requires large annotated datasets, which limits its applicability across domains with varying entity definitions. This paper addresses few-shot NER, aiming to trans…
Domain Adaptationfew-shot-nerFew-shot NERnamed-entity-recognition+3CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognition
Few-shot Named Entity Recognition (NER), the task of identifying named entities with only a limited amount of labeled data, has gained increasing significance in natural language processing. While existing methodologies …
Contrastive Learningfew-shot-nerFew-shot NERLanguage Modeling+7