Cross-language plagiarism detection
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Research on cross-lingual plagiarism detection has attracted attention in recent few years, focusing on text similarity computation across languages.
“Research on crosslingual plagiarism detection has attracted attention in recent few years [20], [27], [31], [36], [38], [108], thus focusing on text similarity computation across languages.”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [31]
It requires much effort to be aware of all potential sources on a given topic and to provide strong evidence against an offender.
“'it requires much effort to be aware of all potential sources on a given topic and to provide strong evidence against an offender' [31].”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [31]
Methods include 1) cross-lingual syntax-based methods, which use character n-grams features for languages that are syntactically similar, such as European languages; 2) cross-lingual dictionary-based methods; 3) cross-lingual semantic-based methods, which use comparable or alignment corpora that exploits the vocabulary correlations; and 4) statistics-based methods.
“Methods include 1) cross-lingual syntax-based methods, which use character n -grams features for languages that are syntactically similar, such as European languages [31]; 2) cross-lingual dictionary-based methods [38]; 3) cross-lingual semantic-based methods, which use comparable or alignment corpora that exploits the vocabulary correlations [31], [37]; and 4) statistics-based methods [36].”
From Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods (2012) · cites this paper as [31]
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Research works printed but not linked: 33
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Cited by
Sorted by citations
Understanding Plagiarism Linguistic Patterns, Textual Features, and Detection Methods
Alzahrani, S. M., Salim, N., & Abraham, A. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2134847 · Cited by 299 · see citing works
State-of-the-art in detecting academic plagiarism
Meuschke, N., & Gipp, B. (2013) · International Journal for Educational Integrity · doi:10.21913/ijei.v9i1.847 · Cited by 112 · see citing works
A New Approach for Cross-Language Plagiarism Analysis
Corezola Pereira, R., Moreira, V. P., & Galante, R. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15998-5_4 · Cited by 35 · see citing works
Roostaee, M., Sadreddini, M. H., & Fakhrahmad, S. M. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.102150 · Cited by 31 · see citing works
- Authors
- Potthast, M., Barrón-Cedeño, A., Stein, B., & Rosso, P.
- Affiliations
- Bauhaus-Universität Weimar (DE)
- Universitat Politècnica de València (ES)
- Corresponding author
- Potthast, Martin
- Venue
- Language Resources and Evaluation
- Published
- 2010-01-30
- Volume
- 45
- Issue
- 1
- Pages
- 45-62
- ISSN
- 1574-020X (ISSN-L)
- 1574-0218
- OpenAlex
- W2028776121
- Cited by
- 286
- References
- 0
- Citations by year
- 2026: 9 · 2025: 7 · 2024: 5 · 2023: 9 · 2022: 16 · 2021: 12 · 2020: 23 · 2019: 18 · 2018: 9 · 2017: 17 · 2016: 20 · 2015: 16 · 2014: 26 · 2013: 19 · 2012: 60
- FWCI
- 82.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Language Resources and Evaluation (publishedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- retrieval models, text ranking, Wikipedia, syntactic similarity
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