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Deep Investigation of Cross-Language Plagiarism Detection Methods
May 24, 2017 ยท Declared Dead ยท ๐ BUCC@ACL
"No code URL or promise found in abstract"
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Authors
Jeremy Ferrero, Laurent Besacier, Didier Schwab, Frederic Agnes
arXiv ID
1705.08828
Category
cs.CL: Computation & Language
Citations
19
Venue
BUCC@ACL
Last Checked
4 months ago
Abstract
This paper is a deep investigation of cross-language plagiarism detection methods on a new recently introduced open dataset, which contains parallel and comparable collections of documents with multiple characteristics (different genres, languages and sizes of texts). We investigate cross-language plagiarism detection methods for 6 language pairs on 2 granularities of text units in order to draw robust conclusions on the best methods while deeply analyzing correlations across document styles and languages.
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