{"url":"/dataset/nyt-h","name":"NYT-H","full_name":null,"description_markdown":"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.\r\n\r\nSource: [Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction](https://www.aclweb.org/anthology/2020.coling-main.566.pdf)","description_withheld":null,"homepage":"https://github.com/Spico197/NYT-H","introduced_date":"2020-10-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/towards-accurate-and-consistent-evaluation-a","title":"Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction","first_author":"Tong Zhu","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"}],"languages":[],"variants":["NYT-H"],"data_loaders":[{"repo":"https://github.com/Spico197/NYT-H","url":"https://github.com/Spico197/NYT-H","frameworks":["pytorch"]}],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}