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

Understand

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.