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Deep Investigation of Cross-Language Plagiarism Detection Methods

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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Contributor : Jérémy Ferrero <>
Submitted on : Thursday, June 1, 2017 - 3:43:45 PM
Last modification on : Wednesday, October 7, 2020 - 3:02:42 AM
Long-term archiving on: : Wednesday, September 6, 2017 - 7:10:53 PM


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  • HAL Id : hal-01531346, version 1


Jérémy Ferrero, Laurent Besacier, Didier Schwab, Frédéric Agnès. Deep Investigation of Cross-Language Plagiarism Detection Methods. BUCC, 10th Workshop on Building and Using Comparable Corpora, Aug 2017, Vancouver, Canada. ⟨hal-01531346⟩



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