Multi-way classification of semantic relations between pairs of nominals

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Matching the Blanks: Distributional Similarity for Relation Learning (2019, methodology): “Similarly, by training by matching the blanks and then tuning on labeled data, we significantly improve performance on the SemEval 2010 Task 8 (Hendrickx et al., 2009), KBP-37 (Zhang and Wang, 2015), and TACRED (Zhang et al., 2017) relation extraction benchmarks.”
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (2019, methodology): “Besides, we use SemEval-2010 task 8 dataset (Hendrickx et al., 2009) as the validation set, since both the corpora and the schema of SemEval-2010 task 8 are in different domains from the original FewRel dataset and the newly-annotated test set.”
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling (2020, methodology): “For sentence-level RE datasets such as TACRED (Zhang et al., 2017) and SemEval 2010 Task 8 (Hendrickx et al., 2009), a sentence only contains one entity pair to classify.”
NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction (2020, methodology): “TACRED [38] 75,049 /25,763 / 18,659 42 270 1,630 SemEval [11] 7,199 /800 / 1,864 19 164 1,454”
A Study of Various Text Augmentation Techniques for Relation Classification in Free Text (2019, background): “The first dataset is SemEval2010 (Hendrickx et al., 2009) which has a total of 10 unique relation classes (ignoring the direction aspect).”