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PAN Plagiarism Corpus 2010 (PAN-PC-10)
- data.niaid.nih.gov
Updated Jan 24, 2020+ more versions
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EmailClick to copy linkLink copiedCitePotthast, Martin; Stein, Benno; Eiselt, Andreas; Barrón-Cedeño, Alberto; Rosso, Paolo (2020). PAN Plagiarism Corpus 2010 (PAN-PC-10) [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_3250122Dataset updatedJan 24, 2020Dataset provided byUniversidad Polytécnica de Valencia
Bauhaus-Universität WeimarAuthorsPotthast, Martin; Stein, Benno; Eiselt, Andreas; Barrón-Cedeño, Alberto; Rosso, PaoloLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThis corpus is outdated. Please use its successor PAN-PC-11: https://doi.org/10.5281/zenodo.3250095 The PAN plagiarism corpus 2010 (PAN-PC-10) is a corpus for the evaluation of automatic plagiarism detection algorithms. For research purposes the corpus can be used free of charge. The PAN-PC-10 contains documents in which artificial plagiarism has been inserted automatically as well as documents in which simulated plagiarism has been inserted manually. The former have been constructed using a so-called random plagiarist, a computer program which constructs plagiarism according to a number of parameters, while the latter have been obtained with crowdsourcing via Amazon's Mechanical Turk.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This corpus is outdated. Please use its successor PAN-PC-11: https://doi.org/10.5281/zenodo.3250095 The PAN plagiarism corpus 2010 (PAN-PC-10) is a corpus for the evaluation of automatic plagiarism detection algorithms. For research purposes the corpus can be used free of charge. The PAN-PC-10 contains documents in which artificial plagiarism has been inserted automatically as well as documents in which simulated plagiarism has been inserted manually. The former have been constructed using a so-called random plagiarist, a computer program which constructs plagiarism according to a number of parameters, while the latter have been obtained with crowdsourcing via Amazon's Mechanical Turk.