Recognizing entities in texts is a central need in many information-seeking
scenarios, and indeed, Named Entity Recognition (NER) is arguably one of the
most successful examples of a widely adopted NLP task and corresponding NLP
technology. Recent advances in large language models (LLMs) appear to provide
effective solutions (also) for NER tasks that were traditionally handled with
dedicated models, often matching or surpassing the abilities of the dedicated
models. Should NER be considered a solved problem? We argue to the contrary:
the capabilities provided by LLMs are not the end of NER research, but rather
an exciting beginning. They allow taking NER to the next level, tackling
increasingly more useful, and increasingly more challenging, variants. We
present three variants of the NER task, together with a dataset to support
them. The first is a move towards more fine-grained -- and intersectional --
entity types. The second is a move towards zero-shot recognition and extraction
of these fine-grained types based on entity-type labels. The third, and most
challenging, is the move from the recognition setup to a novel retrieval setup,
where the query is a zero-shot entity type, and the expected result is all the
sentences from a large, pre-indexed corpus that contain entities of these
types, and their corresponding spans. We show that all of these are far from
being solved. We provide a large, silver-annotated corpus of 4 million
paragraphs covering 500 entity types, to facilitate research towards all of
these three goals.

通过大规模语言模型，命名实体识别可以达到更精细化的实体类型识别、零样本识别和语句检索等目标，但这些目标仍然需要进一步研究和探索。

NERetrieve: 下一代命名实体识别与检索数据集

NERetrieve: Dataset for Next Generation Named Entity Recognition and  Retrieval

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
this https URL

本文提出了 Few-NERD 数据集，并利用其来制定基准任务，从而全面评估模型的泛化能力，结果表明 Few-NERD 具有挑战性并需要进一步研究。