\(k\)nn-ner: Named Entity Recognition With Nearest Neighbor Search
Shuhe Wang, Xiaoya Li, Yuxian Meng, Tianwei Zhang, Rongbin Ouyang, Jiwei Li, Guoyin Wang . Arxiv 2022 – 11 citations
[Code]
[Paper]
Inspired by recent advances in retrieval augmented methods in NLP~\citep{khandelwal2019generalization,khandelwal2020nearest,meng2021gnn}, in this paper, we introduce a (k) nearest neighbor NER ((k)NN-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. (k)NN-NER requires no additional operation during the training phase, and by interpolating (k) nearest neighbors search into the vanilla NER model, (k)NN-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 (k)NN-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.