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UKP Snopes Corpus

datacite.relation.isReferencedBy https://github.com/UKPLab/conll2019-snopes-experiments
datacite.relation.isReferencedBy https://github.com/UKPLab/conll2019-snopes-crawling
dc.contributor.author Hanselowski, Andreas
dc.contributor.author Stab, Christian
dc.contributor.author Schulz, Claudia
dc.contributor.author Li, Zile
dc.contributor.author Gurevych, Iryna
dc.date.accessioned 2019-09-09T13:40:05Z
dc.date.available 2019-09-09T13:40:05Z
dc.date.created 2019-09-09
dc.date.issued 2019-09-09
dc.description This corpus is based on the Snopes fact-checking website and provides annotations for training machine learning models for different tasks in the fact-checking process: document retrieval, stance detection, evidence identification and claim validation. The corpus contains 6,422 validated claims, 16,507 evidence text snippets (annotated with sentence level evidence), and 14,296 documents with their sources (URLs). Please note: We crawled and provide the data according to the regulations of the German text and data mining policy, and we are allowed to share the corpus only for research purposes. Thus, in order to be able to download the corpus, you need to get in contact with us. If you use the corpus in academic works, please cite our CoNLL paper. en_US
dc.identifier.uri https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/2081
dc.language.iso en en_US
dc.rights.licenseIn Copyright (https://rightsstatements.org/vocab/InC/1.0/)
dc.subject Fact-Checking en_US
dc.subject Fake-News en_US
dc.subject Stance Detection en_US
dc.subject Evidence Identification en_US
dc.subject Claim Validation en_US
dc.subject Document Retrieval en_US
dc.title UKP Snopes Corpus en_US
dc.type Dataset en_US
dc.type Text en_US
dcterms.accessRights openAccess
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
person.identifier.orcid 0000-0002-2569-7065
person.identifier.orcid 0000-0002-2994-8191
person.identifier.orcid #PLACEHOLDER_PARENT_METADATA_VALUE#
tuda.tubiblio 116247 en_US

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