少于单次样本：基于极弱监督的命名实体识别
Less than One-shot: Named Entity Recognition via Extremely Weak Supervision
AI总结：
针对仅每类一个无上下文示例的极弱监督NER问题，提出通过比较替换前后上下文分布挖掘伪标签的X-NER方法，性能超越现有单次样本少样本方法及ChatGPT标注，且具备跨语言能力。
摘要：
我们研究极弱监督（XWS）设置下的命名实体识别（NER）问题，该设置中每种实体类型仅提供一个脱离上下文的示例实体。尽管可以看出XWS在监督数据量上比单次样本（one-shot）更少，但我们提出了一种名为X-NER的新方法，其性能能够超过当前最优的单次样本NER方法。我们首先从未标注的训练语料库中挖掘与示例实体相似的实体跨度。我们发现，比较跨度被实体示例替换前后的上下文分布，比利用语言模型生成的实体跨度表示更有效。随后我们利用排名靠前的跨度作为伪标签来训练一个NER标注器。在4个NER数据集上开展的大量实验与分析表明，X-NER的端到端NER性能优异，显著优于采用单次样本监督的当前最优少样本方法以及ChatGPT标注的方法。最后，我们的X-NER具备若干显著特性，例如继承了底层语言模型的跨语言能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
