Deep Investigation of Cross-Language Plagiarism Detection Methods

WS 2017 Jeremy FerreroLaurent BesacierDidier SchwabFrederic Agnes

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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