{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/few-nerd-a-few-shot-named-entity-recognition","title":"Few-NERD: A Few-Shot Named Entity Recognition Dataset","arxiv_id":"2105.07464","date":"2021-05-16","proceeding":"ACL 2021 5","authors":["Ning Ding","Guangwei Xu","Yulin Chen","Xiaobin Wang","Xu Han","Pengjun Xie","Hai-Tao Zheng","Zhiyuan Liu"],"abstract":"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 re-organize them to 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://ningding97.github.io/fewnerd/.","url_abs":"https://arxiv.org/abs/2105.07464v6","url_pdf":"https://arxiv.org/pdf/2105.07464v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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