Methods for cross-language plagiarism detection

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: CrossLanguage 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. * Corresponding author Email addresses: albarron@lsi.upc.edu (Alberto Barrón-Cedeño), pgupta@dsic.upv.es (Parth Gupta), prosso@dsic.upv.es (Paolo Rosso)The authors appear in alphabetical order. A. Barrón and P. Gupta contributed equally to this work and should both be considered as first authors. Preprint submitted to Knowledge Based SystemsJune 14, 2013The 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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