# Denoising Relation Extraction from Document-level Distant Supervision

arXiv: https://arxiv.org/abs/2011.03888 · HTML: https://mlsanity.com/paper/2011.03888/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2020.

## Built on by (most cited first, 19 of 19)

- [Document-level Relation Extraction as Semantic Segmentation](https://mlsanity.com/paper/2106.03618/) (2021)
- [Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction](https://mlsanity.com/paper/2205.12696/) (2022)
- [Packed Levitated Marker for Entity and Relation Extraction](https://mlsanity.com/paper/2109.06067/) (2021)
- [DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction](https://mlsanity.com/paper/2302.08675/) (2023)
- [Multimodal Learning on Graphs for Disease Relation Extraction](https://mlsanity.com/paper/2203.08893/) (2022)
- [Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks](https://mlsanity.com/paper/2310.17238/) (2023)
- [Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs](https://mlsanity.com/paper/2312.00552/) (2023)
- [Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction](https://mlsanity.com/paper/2305.11029/) (2023)
- [A Comprehensive Survey of Document-level Relation Extraction (2016-2023)](https://mlsanity.com/paper/2309.16396/) (2023)
- [GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extraction](https://mlsanity.com/paper/2506.00649/) (2025)
- [An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods](https://mlsanity.com/paper/2207.08286/) (2022)
- [Making Document-Level Information Extraction Right for the Right Reasons](https://mlsanity.com/paper/2110.07686/) (2021)
- [Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labeling](https://mlsanity.com/paper/2212.02036/) (2022)
- [Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data](https://mlsanity.com/paper/2306.09697/) (2023)
- [Comparing and combining some popular NER approaches on Biomedical tasks](https://mlsanity.com/paper/2305.19120/) (2023)
- [Solving Label Variation in Scientific Information Extraction via Multi-Task Learning](https://mlsanity.com/paper/2312.15751/) (2023)
- [Towards a More Generalized Approach in Open Relation Extraction](https://mlsanity.com/paper/2505.22801/) (2025)
- [DOREMI: Optimizing Long Tail Predictions in Document-Level Relation Extraction](https://mlsanity.com/paper/2601.11190/) (2026)
- [Ontology-Driven Structural Regularization for Document-Level Relation Extraction](https://mlsanity.com/paper/2608.20856/) (2026)

## Abstract (arXiv)

Distant supervision (DS) has been widely used to generate auto-labeled data for sentence-level relation extraction (RE), which improves RE performance. However, the existing success of DS cannot be directly transferred to the more challenging document-level relation extraction (DocRE), since the inherent noise in DS may be even multiplied in document level and significantly harm the performance of RE. To address this challenge, we propose a novel pre-trained model for DocRE, which denoises the document-level DS data via multiple pre-training tasks. Experimental results on the large-scale DocRE benchmark show that our model can capture useful information from noisy DS data and achieve promising results.