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Using Local Text Similarity in Pairwise Document Analysis for Monolingual Plagiarism Detection

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Text Processing (FIRE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10478))

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Abstract

Rapid growth of documents and the increased accessibility of electronic documents lead to the need to develop effective tools for detecting plagiarised texts. The task of plagiarism detection entails two main subtasks, suspicious candidate retrieval and pairwise document similarity analysis also called detailed analysis. In this paper we focus on the second subtask. We will report our monolingual plagiarism detection system which is used to process the Persian plagiarism corpus for the task of pairwise document similarity. To retrieve plagiarised passages this paper presents a pairwise plagiarism detection algorithm based on a vector space model considering the proximity of the terms. The proposed framework is applicable in any language and it could also adapted for cross language domain. We evaluate the performance in terms of precision, recall, granularity and Plagdet metrics.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Nava Ehsan & Azadeh Shakery

Authors
  1. Nava Ehsan
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  2. Azadeh Shakery
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Correspondence to Nava Ehsan .

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Editors and Affiliations

  1. DAIICT, Gujarat, India

    Prasenjit Majumder

  2. Indian Statistical Institute, Kolkata, India

    Mandar Mitra

  3. DAIICT, Gujarat, India

    Parth Mehta

  4. DAIICT, Gujarat, India

    Jainisha Sankhavara

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Ehsan, N., Shakery, A. (2018). Using Local Text Similarity in Pairwise Document Analysis for Monolingual Plagiarism Detection. In: Majumder, P., Mitra, M., Mehta, P., Sankhavara, J. (eds) Text Processing. FIRE 2016. Lecture Notes in Computer Science(), vol 10478. Springer, Cham. https://doi.org/10.1007/978-3-319-73606-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-73606-8_9

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  • Online ISBN: 978-3-319-73606-8

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