Understanding plagiarism linguistic patterns, textual features, and detection methods

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dc.contributor.author Alzahrani, Salha M.
dc.contributor.author Salim, Naomie
dc.contributor.author Abraham, Ajith
dc.date.accessioned 2012-03-20T08:49:41Z
dc.date.available 2012-03-20T08:49:41Z
dc.date.issued 2012
dc.identifier.citation IEEE transactions on systems, man, and cybernetics. Part C: Applications and reviews. 2012, vol. 42, issue 2, p. 133-149. cs
dc.identifier.issn 1094-6977
dc.identifier.uri http://hdl.handle.net/10084/90243
dc.description.abstract Plagiarism can be of many different natures, ranging from copying texts to adopting ideas, without giving credit to its originator. This paper presents a new taxonomy of plagiarism that highlights differences between literal plagiarism and intelligent plagiarism, from the plagiarist's behavioral point of view. The taxonomy supports deep understanding of different linguistic patterns in committing plagiarism, for example, changing texts into semantically equivalent but with different words and organization, shortening texts with concept generalization and specification, and adopting ideas and important contributions of others. Different textual features that characterize different plagiarism types are discussed. Systematic frameworks and methods of monolingual, extrinsic, intrinsic, and cross-lingual plagiarism detection are surveyed and correlated with plagiarism types, which are listed in the taxonomy. We conduct extensive study of state-of-the-art techniques for plagiarism detection, including character n-gram-based (CNG), vector-based (VEC), syntax-based (SYN), semantic-based (SEM), fuzzy-based (FUZZY), structural-based (STRUC), stylometric-based (STYLE), and cross-lingual techniques (CROSS). Our study corroborates that existing systems for plagiarism detection focus on copying text but fail to detect intelligent plagiarism when ideas are presented in different words. cs
dc.language.iso en cs
dc.publisher IEEE Systems, Man, and Cybernetics Society cs
dc.relation.ispartofseries IEEE transactions on systems, man, and cybernetics. Part C: Applications and reviews. cs
dc.relation.uri http://dx.doi.org/10.1109/TSMCC.2011.2134847 cs
dc.title Understanding plagiarism linguistic patterns, textual features, and detection methods cs
dc.type article cs
dc.identifier.location Není ve fondu ÚK cs
dc.identifier.doi 10.1109/TSMCC.2011.2134847
dc.type.status Peer-reviewed cs
dc.description.source Web of Science cs
dc.description.volume 42 cs
dc.description.issue 2 cs
dc.description.lastpage 149 cs
dc.description.firstpage 133 cs
dc.identifier.wos 000300511400001

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