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An Evolutionary Neural Network Approach to Intrinsic Plagiarism Detection

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Book cover Artificial Intelligence and Cognitive Science (AICS 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6206))

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Abstract

Intrinsic Plagiarism Detection attempts to identify portions of a document which have been plagiarised without the use of reference collections. This is typically achieved by developing a classifier using support vector machines or hand-crafted neural networks. This paper presents an evolutionary neural network approach to the development of an intrinsic plagiarism detection classifier which is capable of evolving both the weights and structure of a neural network. The neural network is empirically tested on a corpus of documents and is shown to perform well.

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Author information

Authors and Affiliations

  1. Cork Constraint Computation Centre (4C), University College Cork, Ireland

    Dara Curran

Authors
  1. Dara Curran
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Editor information

Editors and Affiliations

  1. Lero, International Science Centre, University of Limerick, Limerick, Ireland

    Lorcan Coyle

  2. CSIRO Tasmanian ICT centre, GPO Box 1538, 7001, Hobart, Tasmania, Australia

    Jill Freyne

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Curran, D. (2010). An Evolutionary Neural Network Approach to Intrinsic Plagiarism Detection. In: Coyle, L., Freyne, J. (eds) Artificial Intelligence and Cognitive Science. AICS 2009. Lecture Notes in Computer Science(), vol 6206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17080-5_6

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  • DOI: https://doi.org/10.1007/978-3-642-17080-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17079-9

  • Online ISBN: 978-3-642-17080-5

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