region:us
Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark
data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and reorganize
them into the few-shot setting for empirical study. These strategies conventionally aim to recognize coarse-grained entity types with few
examples, while in practice, most unseen entity types are fine-grained. In this paper, we present FEW-NERD, a large-scale human-annotated
few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types. FEW-NERD consists of 188,238 sentences from
Wikipedia, 4,601,160 words are included and each is annotated as context or a part of a two-level entity type. To the best of our knowledge,
this is the first few-shot NER dataset and the largest human-crafted NER dataset. We construct benchmark tasks with different emphases to
comprehensively assess the generalization capability of models. Extensive empirical results and analysis show that FEW-NERD is challenging
and the problem requires further research. We make Few-NERD public at https://nigding97.github.io/fewnerd/
@article{ding2021few,
  title={Few-NERD: A Few-Shot Named Entity Recognition Dataset},
  author={Ding, Ning and Xu, Guangwei and Chen, Yulin and Wang, Xiaobin and Han, Xu and Xie, Pengjun and Zheng, Hai-Tao and Liu, Zhiyuan},
  journal={arXiv preprint arXiv:2105.07464},
  year={2021}
}
Entry not found