# kNN-NER: Named Entity Recognition with Nearest Neighbor Search

arXiv: https://arxiv.org/abs/2203.17103 · HTML: https://mlsanity.com/paper/2203.17103/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation.
- Builds on 26 of its 46 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Placed in

- Retrieval-augmented generation / [Generation and context integration](https://mlsanity.com/rag/generation-and-context-integration/)

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2010.00710/) (2020)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [A Unified MRC Framework for Named Entity Recognition](https://mlsanity.com/paper/1910.11476/) (2019)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [Pre-training via Paraphrasing](https://mlsanity.com/paper/2006.15020/) (2020)
- [Retrieve and Refine: Improved Sequence Generation Models For Dialogue](https://mlsanity.com/paper/1808.04776/) (2018)
- [BertGCN: Transductive Text Classification by Combining GCN and BERT](https://mlsanity.com/paper/2105.05727/) (2021)
- [ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://mlsanity.com/paper/2106.16038/) (2021)
- [Augmenting Transformers with KNN-Based Composite Memory for Dialogue](https://mlsanity.com/paper/2004.12744/) (2020)
- [Fast Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2105.14528/) (2021)
- [Efficient Retrieval Augmented Generation from Unstructured Knowledge for Task-Oriented Dialog](https://mlsanity.com/paper/2102.04643/) (2021)
- [Empower Sequence Labeling with Task-Aware Neural Language Model](https://mlsanity.com/paper/1709.04109/) (2017)
- [GNN-LM: Language Modeling based on Global Contexts via GNN](https://mlsanity.com/paper/2110.08743/) (2021)
- [Hierarchically-Refined Label Attention Network for Sequence Labeling](https://mlsanity.com/paper/1908.08676/) (2019)
- [GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling](https://mlsanity.com/paper/1906.02437/) (2019)
- [Dependency Parsing as MRC-based Span-Span Prediction](https://mlsanity.com/paper/2105.07654/) (2021)
- [Hybrid semi-Markov CRF for Neural Sequence Labeling](https://mlsanity.com/paper/1805.03838/) (2018)
- [Faster Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2112.08152/) (2021)

## Built on by (most cited first, 6 of 6)

- [FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios](https://mlsanity.com/paper/2407.08035/) (2024)
- [Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction](https://mlsanity.com/paper/2210.10709/) (2022)
- [Federated Nearest Neighbor Machine Translation](https://mlsanity.com/paper/2302.12211/) (2023)
- [YATO: Yet Another deep learning based Text analysis Open toolkit](https://mlsanity.com/paper/2209.13877/) (2022)
- [End-to-End Entity Detection with Proposer and Regressor](https://mlsanity.com/paper/2210.10260/) (2022)
- [Label Distribution Learning-Enhanced Dual-KNN for Text Classification](https://mlsanity.com/paper/2503.04869/) (2025)

## Abstract (arXiv)

Inspired by recent advances in retrieval augmented methods in NLP~citepkhandelwal2019generalization,khandelwal2020nearest,meng2021gnn, 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 urlhttps://github.com/ShannonAI/KNN-NER.