mlsanityData to 14 Oct 2026

SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals

arXiv 1911.10422 ↗First posted 23 Nov 2019cs.CL cs.AI cs.IRcited by 63

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Abstract (arXiv)

In response to the continuing research interest in computational semantic analysis, we have proposed a new task for SemEval-2010: multi-way classification of mutually exclusive semantic relations between pairs of nominals. The task is designed to compare different approaches to the problem and to provide a standard testbed for future research. In this paper, we define the task, describe the creation of the datasets, and discuss the results of the participating 28 systems submitted by 10 teams.

Key facts. Cited by 63 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Nov 2019.

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Citing papers per year: 2021: 18; 2022: 23; 2023: 10; 2024: 7; 2025: 3; 2026: 2.

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  1. 2021KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extractioncited by 180
  2. 2021OpenPrompt: An Open-source Framework for Prompt-learningcited by 148
  3. 2023Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulnesscited by 130
  4. 2021WRENCH: A Comprehensive Benchmark for Weak Supervisioncited by 96
  5. 2022BioRED: A Rich Biomedical Relation Extraction Datasetcited by 85
  6. 2021When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Datacited by 75
  7. 2023GPT-RE: In-context Learning for Relation Extraction using Large Language Modelscited by 62
  8. 2022Revisiting DocRED -- Addressing the False Negative Problem in Relation Extractioncited by 57
  9. 2023Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractorscited by 45
  10. 2024KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extractioncited by 34
  11. 2021Gradient Imitation Reinforcement Learning for Low Resource Relation Extractioncited by 33
  12. 2022Summarization as Indirect Supervision for Relation Extractioncited by 31
38 more
  1. 2021Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and Systemcited by 30
  2. 2022Should We Rely on Entity Mentions for Relation Extraction? Debiasing Relation Extraction with Counterfactual Analysiscited by 30
  3. 2021A Survey on Extraction of Causal Relations from Natural Language Textcited by 25
  4. 2021SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extractioncited by 23
  5. 2022CrossRE: A Cross-Domain Dataset for Relation Extractioncited by 21
  6. 2022What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classificationcited by 17
  7. 2021Not All Models Localize Linguistic Knowledge in the Same Place: A Layer-wise Probing on BERToids' Representationscited by 16
  8. 2021Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Surveycited by 12
  9. 2021CausalBERT: Injecting Causal Knowledge Into Pre-trained Models with Minimal Supervisioncited by 12
  10. 2021Deep Neural Network Based Relation Extraction: An Overviewcited by 11
  11. 2023GDA: Generative Data Augmentation Techniques for Relation Extraction Taskscited by 10
  12. 2021Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphscited by 9
  13. 2022A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genescited by 9
  14. 2024Retrieval-Augmented Generation-based Relation Extractioncited by 9
  15. 2022Why only Micro-F1? Class Weighting of Measures for Relation Classificationcited by 8
  16. 2023Weaker Than You Think: A Critical Look at Weakly Supervised Learningcited by 8
  17. 2022Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understandingcited by 7
  18. 2024Recall, Retrieve and Reason: Towards Better In-Context Relation Extractioncited by 6
  19. 2024Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworkscited by 6
  20. 2022A Generative Model for Relation Extraction and Classificationcited by 5
  21. 2024IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpuscited by 5
  22. 2024The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoningcited by 5
  23. 2022Implicit N-grams Induced by Recurrencecited by 4
  24. 2022SepLL: Separating Latent Class Labels from Weak Supervision Noisecited by 4
  25. 2022Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022cited by 4
  26. 2025Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Modelscited by 4
  27. 2021D-REX: Dialogue Relation Extraction with Explanationscited by 3
  28. 2021Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipelinecited by 3
  29. 2022REKnow: Enhanced Knowledge for Joint Entity and Relation Extractioncited by 3
  30. 2022An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methodscited by 3
  31. 2023Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extractioncited by 3
  32. 2023Enhancing Low-Resource Relation Representations through Multi-View Decouplingcited by 3
  33. 2024KnowCoder-X: Boosting Multilingual Information Extraction via Codecited by 3
  34. 2021Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognitioncited by 2
  35. 2022GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extractioncited by 2
  36. 2022Span-based joint entity and relation extraction augmented with sequence tagging mechanismcited by 2
  37. 2022Continual Contrastive Finetuning Improves Low-Resource Relation Extractioncited by 2
  38. 2023Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagationcited by 2

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