{"url":"/dataset/semeval-2015-task-1","name":"SemEval-2015 Task-1","full_name":"Paraphrase and Semantic Similarity in Twitter","description_markdown":"Given two sentences, the participants are asked to determine whether they express the same or very similar meaning and optionally a degree score between 0 and 1. Following the literature on paraphrase identification, we evaluate system performance primarily by the F-1 score and Accuracy against human judgments. We also provide additional evaluations by Pearson correlation and PINC (Chen and Dolan, 2011), which measure lexical dissimilarity between sentence pairs.","description_withheld":null,"homepage":"https://alt.qcri.org/semeval2015/task1/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["SemEval-2015 Task-1"],"data_loaders":[],"num_papers_in_archive":0,"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."}