Adaptive Algorithm for Plagiarism Detection: The Best-Performing Approach at PAN 2014 Text Alignment Competition

  • Miguel A. Sanchez-Perez
  • Alexander Gelbukh
  • Grigori Sidorov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9283)


The task of (monolingual) text alignment consists in finding similar text fragments between two given documents. It has applications in plagiarism detection, detection of text reuse, author identification, authoring aid, and information retrieval, to mention only a few. We describe our approach to the text alignment subtask of the plagiarism detection competition at PAN 2014, which resulted in the best-performing system at the PAN 2014 competition and outperforms the best-performing system of the PAN 2013 competition by the cumulative evaluation measure Plagdet. Our method relies on a sentence similarity measure based on a tf-idf-like weighting scheme that permits us to consider stopwords without increasing the rate of false positives. We introduce a recursive algorithm to extend the ranges of matching sentences to maximal length passages. We also introduce a novel filtering method to resolve overlapping plagiarism cases. Our system is available as open source.


Adaptive Algorithm Vector Space Model Source Document Training Corpus Computational Linguistics 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Miguel A. Sanchez-Perez
    • 1
  • Alexander Gelbukh
    • 1
  • Grigori Sidorov
    • 1
  1. 1.Centro de Investigacin en ComputacinInstituto Politcnico NacionalMexico CityMexico

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