# Rescue Implicit and Long-tail Cases: Nearest Neighbor Relation Extraction

arXiv: https://arxiv.org/abs/2210.11800 · HTML: https://mlsanity.com/paper/2210.11800/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 25 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://mlsanity.com/paper/2010.03851/) (2020)
- [An Improved Baseline for Sentence-level Relation Extraction](https://mlsanity.com/paper/2102.01373/) (2021)
- [Learning from Context or Names? An Empirical Study on Neural Relation Extraction](https://mlsanity.com/paper/2010.01923/) (2020)
- [OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction](https://mlsanity.com/paper/1909.13078/) (2019)
- [RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://mlsanity.com/paper/1812.04361/) (2018)
- [CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning](https://mlsanity.com/paper/1911.10438/) (2019)
- [A Frustratingly Easy Approach for Entity and Relation Extraction](https://mlsanity.com/paper/2010.12812/) (2020)
- [Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction](https://mlsanity.com/paper/2009.07503/) (2020)
- [GNN-LM: Language Modeling based on Global Contexts via GNN](https://mlsanity.com/paper/2110.08743/) (2021)
- [CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2106.10855/) (2021)
- [WebRED: Effective Pretraining And Finetuning For Relation Extraction On The Web](https://mlsanity.com/paper/2102.09681/) (2021)
- [Relation Extraction with Weighted Contrastive Pre-training on Distant Supervision](https://mlsanity.com/paper/2205.08770/) (2022)

## Built on by (most cited first, 7 of 7)

- [GPT-RE: In-context Learning for Relation Extraction using Large Language Models](https://mlsanity.com/paper/2305.02105/) (2023)
- [Nearest Neighbor Search over Vectorized Lexico-Syntactic Patterns for Relation Extraction from Financial Documents](https://mlsanity.com/paper/2310.17714/) (2023)
- [AMR-RE: Abstract Meaning Representations for Retrieval-Based In-Context Learning in Relation Extraction](https://mlsanity.com/paper/2406.10432/) (2024)
- [End-to-End Trainable Retrieval-Augmented Generation for Relation Extraction](https://mlsanity.com/paper/2406.03790/) (2024)
- [SememeLM: A Sememe Knowledge Enhanced Method for Long-tail Relation Representation](https://mlsanity.com/paper/2406.10297/) (2024)
- [A Debiased Nearest Neighbors Framework for Multi-Label Text Classification](https://mlsanity.com/paper/2408.03202/) (2024)
- [SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN](https://mlsanity.com/paper/2507.06895/) (2025)

## Abstract (arXiv)

Relation extraction (RE) has achieved remarkable progress with the help of pre-trained language models. However, existing RE models are usually incapable of handling two situations: implicit expressions and long-tail relation types, caused by language complexity and data sparsity. In this paper, we introduce a simple enhancement of RE using k nearest neighbors (kNN-RE). kNN-RE allows the model to consult training relations at test time through a nearest-neighbor search and provides a simple yet effective means to tackle the two issues above. Additionally, we observe that kNN-RE serves as an effective way to leverage distant supervision (DS) data for RE. Experimental results show that the proposed kNN-RE achieves state-of-the-art performances on a variety of supervised RE datasets, i.e., ACE05, SciERC, and Wiki80, along with outperforming the best model to date on the i2b2 and Wiki80 datasets in the setting of allowing using DS. Our code and models are available at: https://github.com/YukinoWan/kNN-RE.