# Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs

arXiv: https://arxiv.org/abs/2312.00552 · HTML: https://mlsanity.com/paper/2312.00552/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction](https://mlsanity.com/paper/2109.03659/) (2021)
- [Joint Entity and Relation Extraction with Set Prediction Networks](https://mlsanity.com/paper/2011.01675/) (2020)
- [SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction](https://mlsanity.com/paper/2004.02438/) (2020)
- [HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction](https://mlsanity.com/paper/2205.02225/) (2022)
- [Summarization as Indirect Supervision for Relation Extraction](https://mlsanity.com/paper/2205.09837/) (2022)
- [Denoising Relation Extraction from Document-level Distant Supervision](https://mlsanity.com/paper/2011.03888/) (2020)
- [Revisiting Unsupervised Relation Extraction](https://mlsanity.com/paper/2005.00087/) (2020)
- [Element Intervention for Open Relation Extraction](https://mlsanity.com/paper/2106.09558/) (2021)
- [MORE: A Metric Learning Based Framework for Open-domain Relation Extraction](https://mlsanity.com/paper/2206.00289/) (2022)

## Built on by (most cited first, 10 of 10)

- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.08690/) (2025)
- [Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach](https://mlsanity.com/paper/2402.14948/) (2024)
- [SNaRe: Domain-aware Data Generation for Low-Resource Event Detection](https://mlsanity.com/paper/2502.17394/) (2025)
- [Single Ground Truth Is Not Enough: Adding Flexibility to Aspect-Based Sentiment Analysis Evaluation](https://mlsanity.com/paper/2410.09807/) (2024)
- [Towards a More Generalized Approach in Open Relation Extraction](https://mlsanity.com/paper/2505.22801/) (2025)
- [Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning Applied to Few-Shot Relation Extraction](https://mlsanity.com/paper/2601.20803/) (2026)
- [Bridge: A Unified Framework to Knowledge Graph Completion via Language Models and Knowledge Representation](https://mlsanity.com/paper/2411.06660/) (2024)
- [LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models](https://mlsanity.com/paper/2509.15089/) (2025)
- [CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation](https://mlsanity.com/paper/2610.12580/) (2026)

## Abstract (arXiv)

Unsupervised relation extraction (URE) aims to extract relations between named entities from raw text without requiring manual annotations or pre-existing knowledge bases. In recent studies of URE, researchers put a notable emphasis on contrastive learning strategies for acquiring relation representations. However, these studies often overlook two important aspects: the inclusion of diverse positive pairs for contrastive learning and the exploration of appropriate loss functions. In this paper, we propose AugURE with both within-sentence pairs augmentation and augmentation through cross-sentence pairs extraction to increase the diversity of positive pairs and strengthen the discriminative power of contrastive learning. We also identify the limitation of noise-contrastive estimation (NCE) loss for relation representation learning and propose to apply margin loss for sentence pairs. Experiments on NYT-FB and TACRED datasets demonstrate that the proposed relation representation learning and a simple K-Means clustering achieves state-of-the-art performance.