Deep Investigation of Cross-Language Plagiarism Detection Methods
This work addresses the problem of detecting plagiarism across different languages for researchers and practitioners, but it is incremental as it applies existing methods to a new dataset.
The paper tackled cross-language plagiarism detection by evaluating methods on a new open dataset with parallel and comparable documents across multiple languages and genres, resulting in robust conclusions on the best methods through analysis of correlations across document styles and languages.
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.