{"url":"/dataset/productreviews2017","name":"Product Reviews 2017","full_name":null,"description_markdown":"The corpus contains review sentences mostly of products in electronics domain, annotated and segregated into 4 comparison categories. Each comparison sentence is annotated with names of the products (PROD1 and PROD2), the aspect (ASP) and the predicate (PRED). Dataset contains sentences after auto-labeling on [SNAP dataset](https://snap.stanford.edu/data/web-Amazon-links.html) and manually labeled sentences from the following corpora:\r\n\r\n- Jindal and Liu, 2006\r\n\r\n- Kessler and Kuhn, 2014\r\n\r\n- JDPA Corpus (Kessler et al, 2010)","description_withheld":null,"homepage":"https://zenodo.org/records/1415481#.W5pjkBwScnQ","introduced_date":"2017-11-06","introduced_date_note":null,"introduced_by":null,"license":{"name":"Creative Commons Attribution 4.0 International","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"},{"name":"Semantic Role Labeling","url":"/task/semantic-role-labeling","datasets_with_task":"/datasets/task/semantic-role-labeling"},{"name":"Predicate Detection","url":"/task/predicate-detection","datasets_with_task":"/datasets/task/predicate-detection"},{"name":"Semantic Role Labeling (predicted predicates)","url":"/task/semantic-role-labeling-predicted-predicates","datasets_with_task":"/datasets/task/semantic-role-labeling-predicted-predicates"},{"name":"Entity Retrieval","url":"/task/entity-retrieval","datasets_with_task":"/datasets/task/entity-retrieval"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Product Reviews 2017"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/predicate-detection-on-product-reviews-2017","task":"Predicate Detection","dataset_variant":"Product Reviews 2017","rows":2,"metrics":["F1 score"],"first_row_in_archive_order":{"model":"Bidirectional-LSTM","paper":"/paper/extracting-entities-of-interest-from-1","metrics":{"F1 score":"50.4"},"code_links":[{"title":"jatinarora2702/Review-Information-Extraction","url":"https://github.com/jatinarora2702/Review-Information-Extraction"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/extracting-entities-of-interest-from-1","title":"Extracting Entities of Interest from Comparative Product Reviews","date":"2023-10-31","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}