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

Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning