Three reasons make plagiarism across languages to be on the rise: (i) speakers of under-resourced languages often consult documentation in a foreign language, (ii) people immersed in a foreign country can still consult material written in their native language, and (iii) people are often interested in writing in a language different to their native one. Most efforts for automatically detecting cross-language plagiarism depend on a preliminary translation, which is not always available. In this paper we propose a freely available architecture for plagiarism detection across languages covering the entire process: heuristic retrieval, detailed analysis, and post-processing. On top of this architecture we explore the suitability of three cross-language similarity estimation models: Cross-Language Alignment-based Similarity Analysis (CL-ASA), Cross-Language Character n-Grams (CL-CNG), and Translation plus Monolingual Analysis (T + MA); three inherently different models in nature and required resources. The three models are tested extensively under the same conditions on the different plagiarism detection sub-tasks - something never done before. The experiments show that T + MA produces the best results, closely followed by CL-ASA. Still CL-ASA obtains higher values of precision, an important factor in plagiarism detection when lesser user intervention is desired. extcopyright 2013 Elsevier B.V. All rights reserved.

Methods for cross-language plagiarism detection

Barrón-Cedeño, Alberto;
2013

Abstract

Three reasons make plagiarism across languages to be on the rise: (i) speakers of under-resourced languages often consult documentation in a foreign language, (ii) people immersed in a foreign country can still consult material written in their native language, and (iii) people are often interested in writing in a language different to their native one. Most efforts for automatically detecting cross-language plagiarism depend on a preliminary translation, which is not always available. In this paper we propose a freely available architecture for plagiarism detection across languages covering the entire process: heuristic retrieval, detailed analysis, and post-processing. On top of this architecture we explore the suitability of three cross-language similarity estimation models: Cross-Language Alignment-based Similarity Analysis (CL-ASA), Cross-Language Character n-Grams (CL-CNG), and Translation plus Monolingual Analysis (T + MA); three inherently different models in nature and required resources. The three models are tested extensively under the same conditions on the different plagiarism detection sub-tasks - something never done before. The experiments show that T + MA produces the best results, closely followed by CL-ASA. Still CL-ASA obtains higher values of precision, an important factor in plagiarism detection when lesser user intervention is desired. extcopyright 2013 Elsevier B.V. All rights reserved.
2013
Barrón-Cedeño, Alberto and Gupta, Parth and Rosso, Paolo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/707694
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