mlsanityData to 7 Oct 2026

More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction

arXiv 2004.03186 ↗First posted 7 Apr 2020cs.CLcited by 52

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Abstract (arXiv)

Relational facts are an important component of human knowledge, which are hidden in vast amounts of text. In order to extract these facts from text, people have been working on relation extraction (RE) for years. From early pattern matching to current neural networks, existing RE methods have achieved significant progress. Yet with explosion of Web text and emergence of new relations, human knowledge is increasing drastically, and we thus require "more" from RE: a more powerful RE system that can robustly utilize more data, efficiently learn more relations, easily handle more complicated context, and flexibly generalize to more open domains. In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE. We hope our view can advance this field and inspire more efforts in the community.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2020.

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Citing papers per year: 2021: 11; 2022: 11; 2023: 12; 2024: 13; 2025: 3; 2026: 2.

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  1. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  2. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 97
  3. 2022Revisiting DocRED -- Addressing the False Negative Problem in Relation Extractioncited by 57
  4. 2022Structured information extraction from complex scientific text with fine-tuned large language modelscited by 56
  5. 2024KG-RAG: Bridging the Gap Between Knowledge and Creativitycited by 55
  6. 2021Curriculum-Meta Learning for Order-Robust Continual Relation Extractioncited by 34
  7. 2023Revisiting Large Language Models as Zero-shot Relation Extractorscited by 29
  8. 2021A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extractioncited by 26
  9. 2024ADELIE: Aligning Large Language Models on Information Extractioncited by 24
  10. 2023A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontierscited by 21
  11. 2021Entity Concept-enhanced Few-shot Relation Extractioncited by 14
  12. 2022Multi-hop Evidence Retrieval for Cross-document Relation Extractioncited by 13
38 more
  1. 2023RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extractioncited by 13
  2. 2021Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Surveycited by 12
  3. 2021Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extractioncited by 12
  4. 2023Construction of Knowledge Graphs: State and Challengescited by 12
  5. 2021Deep Neural Network Based Relation Extraction: An Overviewcited by 11
  6. 2022Towards Relation Extraction From Speechcited by 10
  7. 2022Cross-lingual Lifelong Learningcited by 9
  8. 2024Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extractioncited by 9
  9. 2021Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priorscited by 8
  10. 2022A Survey on Open Information Extraction from Rule-based Model to Large Language Modelcited by 8
  11. 2021PARE: A Simple and Strong Baseline for Monolingual and Multilingual Distantly Supervised Relation Extractioncited by 4
  12. 2022Information Extraction and Human-Robot Dialogue towards Real-life Tasks: A Baseline Study with the MobileCS Datasetcited by 4
  13. 2021Predicting Document Coverage for Relation Extractioncited by 3
  14. 2023PromptORE -- A Novel Approach Towards Fully Unsupervised Relation Extractioncited by 3
  15. 2024Relation Extraction Using Large Language Models: A Case Study on Acupuncture Point Locationscited by 3
  16. 2021Making Document-Level Information Extraction Right for the Right Reasonscited by 2
  17. 2022MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extractioncited by 2
  18. 2023COMBO: A Complete Benchmark for Open KG Canonicalizationcited by 2
  19. 2023Document-Level In-Context Few-Shot Relation Extraction via Pre-Trained Language Modelscited by 2
  20. 2024Construction of Hyper-Relational Knowledge Graphs Using Pre-Trained Large Language Modelscited by 2
  21. 2024TTM-RE: Memory-Augmented Document-Level Relation Extractioncited by 2
  22. 2025Schema as Parameterized Tools for Universal Information Extractioncited by 2
  23. 2021Multi-facet Universal Schemacited by 1
  24. 2022NC-DRE: Leveraging Non-entity Clue Information for Document-level Relation Extractioncited by 1
  25. 2023Open Set Relation Extraction via Unknown-Aware Trainingcited by 1
  26. 2024Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documentscited by 1
  27. 2024Fusion Makes Perfection: An Efficient Multi-Grained Matching Approach for Zero-Shot Relation Extractioncited by 1
  28. 2022Knowledge-Enhanced Relation Extraction Datasetcited by 0
  29. 2023Mining Healthcare Procurement Data Using Text Mining and Natural Language Processing -- Reflection From An Industrial Projectcited by 0
  30. 2023TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extractioncited by 0
  31. 2023A Few-Shot Learning Focused Survey on Recent Named Entity Recognition and Relation Classification Methodscited by 0
  32. 2023Relation Extraction Model Based on Semantic Enhancement Mechanismcited by 0
  33. 2024Distantly Supervised Morpho-Syntactic Model for Relation Extractioncited by 0
  34. 2024READ: Improving Relation Extraction from an ADversarial Perspectivecited by 0
  35. 2024Maximizing Relation Extraction Potential: A Data-Centric Study to Unveil Challenges and Opportunitiescited by 0
  36. 2024Neon: News Entity-Interaction Extraction for Enhanced Question Answeringcited by 0
  37. 2025Dynamic Relation Inference via Verb Embeddingscited by 0
  38. 2025EMERGE: A Benchmark for Updating Knowledge Graphs with Emerging Textual Knowledgecited by 0

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