Methods for cross-language plagiarism detection
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...
| Autores: | , , |
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| Formato: | artículo |
| Fecha de publicación: | 2013 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/38501 |
| Acesso em linha: | https://riunet.upv.es/handle/10251/38501 |
| Access Level: | acceso abierto |
| Palavra-chave: | Automatic plagiarism detection Cross-language plagiarism Plagiarism detection architecture Cross-language similarity Text re-use analysis LENGUAJES Y SISTEMAS INFORMATICOS |
| Resumo: | 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. |
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