kNN-NER: Named Entity Recognition with Nearest Neighbor Search
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Inspired by recent advances in retrieval augmented methods in NLP <cit.>, in this paper, we introduce a k nearest neighbor NER (kNN-NER) framework, which augments the distribution of entity labels by assigning k nearest neighbors retrieved from the training set. This strategy makes the model more capable of handling long-tail cases, along with better few-shot learning abilities. kNN-NER requires no additional operation during the training phase, and by interpolating k nearest neighbors search into the vanilla NER model, kNN-NER consistently outperforms its vanilla counterparts: we achieve a new state-of-the-art F1-score of 72.03 (+1.25) on the Chinese Weibo dataset and improved results on a variety of widely used NER benchmarks. Additionally, we show that kNN-NER can achieve comparable results to the vanilla NER model with 40% less amount of training data. Code available at <https://github.com/ShannonAI/KNN-NER>.
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