We study the named entity recognition (NER) problem under the extremely weak
supervision (XWS) setting, where only one example entity per type is given in a
context-free way. While one can see that XWS is lighter than one-shot in terms
of the amount of supervision, we propose a novel method X-NER that can
outperform the state-of-the-art one-shot NER methods. We first mine entity
spans that are similar to the example entities from an unlabelled training
corpus. Instead of utilizing entity span representations from language models,
we find it more effective to compare the context distributions before and after
the span is replaced by the entity example. We then leverage the top-ranked
spans as pseudo-labels to train an NER tagger. Extensive experiments and
analyses on 4 NER datasets show the superior end-to-end NER performance of
X-NER, outperforming the state-of-the-art few-shot methods with 1-shot
supervision and ChatGPT annotations significantly. Finally, our X-NER possesses
several notable properties, such as inheriting the cross-lingual abilities of
the underlying language models.

我们研究了在极弱监督条件下的命名实体识别（NER）问题，提出了一种名为 X-NER 的新方法，可以胜过现有的一次性 NER 方法，具有跨语言能力。