# Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs arXiv: https://arxiv.org/abs/2105.11225 ยท HTML: https://mlsanity.com/paper/2105.11225/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 68 references that are in the map. - First posted on arXiv 24 May 2021. ## Builds on (most cited first, 28 of 28) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017) - [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017) - [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018) - [Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://mlsanity.com/paper/1703.04826/) (2017) - [FewRel 2.0: Towards More Challenging Few-Shot Relation Classification](https://mlsanity.com/paper/1910.07124/) (2019) - [Learning from Context or Names? An Empirical Study on Neural Relation Extraction](https://mlsanity.com/paper/2010.01923/) (2020) - [SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals](https://mlsanity.com/paper/1911.10422/) (2019) - [Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks](https://mlsanity.com/paper/1903.01306/) (2019) - [RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://mlsanity.com/paper/1812.04361/) (2018) - [Reinforcement Learning for Relation Classification from Noisy Data](https://mlsanity.com/paper/1808.08013/) (2018) - [A Frustratingly Easy Approach for Entity and Relation Extraction](https://mlsanity.com/paper/2010.12812/) (2020) - [Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction](https://mlsanity.com/paper/1906.08646/) (2019) - [Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention](https://mlsanity.com/paper/1804.06987/) (2018) - [Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions](https://mlsanity.com/paper/1904.00143/) (2019) - [DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction](https://mlsanity.com/paper/1805.09929/) (2018) - [Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/1911.11899/) (2019) - [Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning](https://mlsanity.com/paper/2010.15877/) (2020) - [Are Noisy Sentences Useless for Distant Supervised Relation Extraction?](https://mlsanity.com/paper/1911.09788/) (2019) - [Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector](https://mlsanity.com/paper/1811.05616/) (2018) - [Learning Relation Prototype from Unlabeled Texts for Long-tail Relation Extraction](https://mlsanity.com/paper/2011.13574/) (2020) - [Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors](https://mlsanity.com/paper/2104.08225/) (2021) - [Distant Supervision for Relation Extraction with Linear Attenuation Simulation and Non-IID Relevance Embedding](https://mlsanity.com/paper/1812.09516/) (2018) - [Structured Minimally Supervised Learning for Neural Relation Extraction](https://mlsanity.com/paper/1904.00118/) (2019) - [Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention](https://mlsanity.com/paper/2010.03773/) (2020) - [Improving Neural Relation Extraction with Implicit Mutual Relations](https://mlsanity.com/paper/1907.05333/) (2019) - [Improving Performance of Relation Extraction Algorithm via Leveled Adversarial PCNN and Database Expansion](https://mlsanity.com/paper/2007.15084/) (2020) ## Built on by (most cited first, 9 of 9) - [On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models](https://mlsanity.com/paper/2406.16367/) (2024) - [Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery](https://mlsanity.com/paper/2401.06327/) (2024) - [Sentence Bag Graph Formulation for Biomedical Distant Supervision Relation Extraction](https://mlsanity.com/paper/2310.18912/) (2023) - [Data-Centric Long-Tailed Image Recognition](https://mlsanity.com/paper/2311.01744/) (2023) - [Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier](https://mlsanity.com/paper/2501.05155/) (2025) - [Knowledge-Enhanced Relation Extraction Dataset](https://mlsanity.com/paper/2210.11231/) (2022) - [SememeLM: A Sememe Knowledge Enhanced Method for Long-tail Relation Representation](https://mlsanity.com/paper/2406.10297/) (2024) - [Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin](https://mlsanity.com/paper/2410.20320/) (2024) - [Ontology-Driven Structural Regularization for Document-Level Relation Extraction](https://mlsanity.com/paper/2608.20856/) (2026) ## Abstract (arXiv) Label noise and long-tailed distributions are two major challenges in distantly supervised relation extraction. Recent studies have shown great progress on denoising, but paid little attention to the problem of long-tailed relations. In this paper, we introduce a constraint graph to model the dependencies between relation labels. On top of that, we further propose a novel constraint graph-based relation extraction framework(CGRE) to handle the two challenges simultaneously. CGRE employs graph convolution networks to propagate information from data-rich relation nodes to data-poor relation nodes, and thus boosts the representation learning of long-tailed relations. To further improve the noise immunity, a constraint-aware attention module is designed in CGRE to integrate the constraint information. Extensive experimental results indicate that CGRE achieves significant improvements over the previous methods for both denoising and long-tailed relation extraction. The pre-processed datasets and source code are publicly available at https://github.com/tmliang/CGRE.