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Denoising Relation Extraction from Document-level Distant Supervision

arXiv 2011.03888 ↗First posted 8 Nov 2020cs.CLcited by 19

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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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Nov 2020.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 8; 2024: 0; 2025: 2; 2026: 2.

19 papers.

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  1. 2021Document-level Relation Extraction as Semantic Segmentationcited by 80
  2. 2022Revisiting DocRED -- Addressing the False Negative Problem in Relation Extractioncited by 57
  3. 2021Packed Levitated Marker for Entity and Relation Extractioncited by 49
  4. 2023DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extractioncited by 37
  5. 2022Multimodal Learning on Graphs for Disease Relation Extractioncited by 15
  6. 2023Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networkscited by 11
  7. 2023Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairscited by 9
  8. 2023Uncertainty Guided Label Denoising for Document-level Distant Relation Extractioncited by 7
  9. 2023A Comprehensive Survey of Document-level Relation Extraction (2016-2023)cited by 4
  10. 2022An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methodscited by 3
  11. 2025GuideX: Guided Synthetic Data Generation for Zero-Shot Information Extractioncited by 3
  12. 2021Making Document-Level Information Extraction Right for the Right Reasonscited by 2
7 more
  1. 2022Query Your Model with Definitions in FrameNet: An Effective Method for Frame Semantic Role Labelingcited by 2
  2. 2023Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Datacited by 2
  3. 2023Comparing and combining some popular NER approaches on Biomedical taskscited by 1
  4. 2023Solving Label Variation in Scientific Information Extraction via Multi-Task Learningcited by 1
  5. 2025Towards a More Generalized Approach in Open Relation Extractioncited by 1
  6. 2026DOREMI: Optimizing Long Tail Predictions in Document-Level Relation Extractioncited by 0
  7. 2026Ontology-Driven Structural Regularization for Document-Level Relation Extractioncited by 0