Filtering Documents for Plagiarism Detection

Kensuke Baba

doi:10.1007/978-3-030-01771-2_23

Filtering Documents for Plagiarism Detection

Kensuke Baba

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Efficient methods are required for plagiarism detection. This paper proposes a fast and scalable method for detecting “copy and paste”-type plagiarism in documents. Implementing detection methods for this type of plagiarism requires a long processing time or a large database for comprehensive matching of ordered word occurrences. The author improved the scalability of an existing fast method based on fast Fourier transform using the idea of the frequency domain filtering. He evaluated the effect of the improvement on accuracy of the plagiarism detection method, and achieved an effective trade-off between the accuracy and the required size of database.

Original language	English
Title of host publication	Discovery Science - 21st International Conference, DS 2018, Proceedings
Editors	Michelangelo Ceci, Larisa Soldatova, Joaquin Vanschoren, George Papadopoulos
Publisher	Springer Verlag
Pages	361-372
Number of pages	12
ISBN (Print)	9783030017705
DOIs	https://doi.org/10.1007/978-3-030-01771-2_23
Publication status	Published - 2018
Externally published	Yes
Event	21st International Conference on Discovery Science, DS 2018 - Limassol, Cyprus Duration: Oct 29 2018 → Oct 31 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11198 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Discovery Science, DS 2018
Country/Territory	Cyprus
City	Limassol
Period	10/29/18 → 10/31/18

Keywords

Fast Fourier transform
Filtering
Plagiarism detection
Text processing
Vector representation of words

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-01771-2_23

Cite this

Baba, K. (2018). Filtering Documents for Plagiarism Detection. In M. Ceci, L. Soldatova, J. Vanschoren, & G. Papadopoulos (Eds.), Discovery Science - 21st International Conference, DS 2018, Proceedings (pp. 361-372). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11198 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-01771-2_23

Filtering Documents for Plagiarism Detection. / Baba, Kensuke.
Discovery Science - 21st International Conference, DS 2018, Proceedings. ed. / Michelangelo Ceci; Larisa Soldatova; Joaquin Vanschoren; George Papadopoulos. Springer Verlag, 2018. p. 361-372 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11198 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Baba, K 2018, Filtering Documents for Plagiarism Detection. in M Ceci, L Soldatova, J Vanschoren & G Papadopoulos (eds), Discovery Science - 21st International Conference, DS 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11198 LNAI, Springer Verlag, pp. 361-372, 21st International Conference on Discovery Science, DS 2018, Limassol, Cyprus, 10/29/18. https://doi.org/10.1007/978-3-030-01771-2_23

Baba K. Filtering Documents for Plagiarism Detection. In Ceci M, Soldatova L, Vanschoren J, Papadopoulos G, editors, Discovery Science - 21st International Conference, DS 2018, Proceedings. Springer Verlag. 2018. p. 361-372. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-01771-2_23

Baba, Kensuke. / Filtering Documents for Plagiarism Detection. Discovery Science - 21st International Conference, DS 2018, Proceedings. editor / Michelangelo Ceci ; Larisa Soldatova ; Joaquin Vanschoren ; George Papadopoulos. Springer Verlag, 2018. pp. 361-372 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Efficient methods are required for plagiarism detection. This paper proposes a fast and scalable method for detecting “copy and paste”-type plagiarism in documents. Implementing detection methods for this type of plagiarism requires a long processing time or a large database for comprehensive matching of ordered word occurrences. The author improved the scalability of an existing fast method based on fast Fourier transform using the idea of the frequency domain filtering. He evaluated the effect of the improvement on accuracy of the plagiarism detection method, and achieved an effective trade-off between the accuracy and the required size of database.",

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AB - Efficient methods are required for plagiarism detection. This paper proposes a fast and scalable method for detecting “copy and paste”-type plagiarism in documents. Implementing detection methods for this type of plagiarism requires a long processing time or a large database for comprehensive matching of ordered word occurrences. The author improved the scalability of an existing fast method based on fast Fourier transform using the idea of the frequency domain filtering. He evaluated the effect of the improvement on accuracy of the plagiarism detection method, and achieved an effective trade-off between the accuracy and the required size of database.

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Filtering Documents for Plagiarism Detection

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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