Abstract
Current research in the field of automatic plagiarism detection for text documents focuses on algorithms that compare plagiarized documents against potential original documents. Though these approaches perform well in identifying copied or even modified passages, they assume a closed world: a reference collection must be given against which a plagiarized document can be compared.
This raises the question whether plagiarized passages within a document can be detected automatically if no reference is given, e. g. if the plagiarized passages stem from a book that is not available in digital form. We call this problem class intrinsic plagiarism detection. The paper is devoted to this problem class; it shows that it is possible to identify potentially plagiarized passages by analyzing a single document with respect to variations in writing style.
Our contributions are fourfold: (i) a taxonomy of plagiarism delicts along with detection methods, (ii) new features for the quantification of style aspects, (iii) a publicly available plagiarism corpus for benchmark comparisons, and (iv) promising results in non-trivial plagiarism detection settings: in our experiments we achieved recall values of 85% with a precision of 75% and better.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brin, S., Davis, J., Garcia-Molina, H.: Copy detection mechanisms for digital documents. In: Proc. SIGMOD 1995, pp. 398–409 (1995)
Garside, R., Leech, G., McEnery, A.: Corpus Annotation: Linguistic Information from Computer Text Corpora. Longman (1997)
Hoad, T.C., Zobel, J.: Methods for Identifying Versioned and Plagiarised Documents. JASIST 54(3), 203–215 (2003)
Koppel, M., Schler, J.: Authorship verification as a one-class classification problem. In: Proc. of ICML (2004)
McCabe, D.: Research Report of the Center for Academic Integrity (2005), http://www.academicintegrity.org
Meyer zu Eißen, S., Stein, B.: Genre Classification of Web Pages: User Study and Feasibility Analysis. In: Biundo, S., Frühwirth, T., Palm, G. (eds.) KI 2004. LNCS (LNAI), vol. 3238, pp. 256–269. Springer, Heidelberg (2004)
Stein, B.: Fuzzy-Fingerprints for Text-based Information Retrieval. In: Proc. of 5th Int. Conf. on Knowledge Management, Graz, Austria. JUCS (2005)
Stein, B., Meyer, S.: Near similarity search and plagiarism analysis. In: Proc. of GfKl 2005. Springer, Heidelberg (2005)
University of Leipzig. Wortschatz (1995), http://wortschatz.uni-leipzig.de
Wikipedia. Plagiarism (2005), http://en.wikipedia.org/wiki/Plagiarism
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Eissen, S.M.z., Stein, B. (2006). Intrinsic Plagiarism Detection. In: Lalmas, M., MacFarlane, A., Rüger, S., Tombros, A., Tsikrika, T., Yavlinsky, A. (eds) Advances in Information Retrieval. ECIR 2006. Lecture Notes in Computer Science, vol 3936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11735106_66
Download citation
DOI: https://doi.org/10.1007/11735106_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33347-0
Online ISBN: 978-3-540-33348-7
eBook Packages: Computer ScienceComputer Science (R0)