We present a simple few-shot named entity recognition (NER) system based on
nearest neighbor learning and structured inference. Our system uses a
supervised NER model trained on the source domain, as a feature extractor.
Across several test domains, we show that a nearest neighbor classifier in this
feature-space is far more effective than the standard meta-learning approaches.
We further propose a cheap but effective method to capture the label
dependencies between entity tags without expensive CRF training. We show that
our method of combining structured decoding with nearest neighbor learning
achieves state-of-the-art performance on standard few-shot NER evaluation
tasks, improving F1 scores by $6\%$ to $16\%$ absolute points over prior
meta-learning based systems.

论文提出了一个基于最近邻学习和结构化推理的简单的少样本命名实体识别系统，使用一个有监督的 NER 模型在源领域上进行训练，作为特征提取器，提出了一种捕获实体标签之间标签依赖性的廉价但有效的方法，并将结构化解码与最近邻学习相结合，达到了先进的表现。该方法使得 F1 分数相对于以前基于元学习的系统提高了 6％至 16％的绝对值。