  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.
