NYT-H
Introduced by Zhu et al. in Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
NYT-H is a dataset for distantly-supervised relation extraction, in which DS-labelled training data is used and several annotators to label test data are hired. NYT-H can serve as a benchmark of distantly-supervised relation extraction.
Source: Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation ExtractionPapers
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