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Machine Learning Method for Paraphrase Identification

  • Oleksandr Marchenko
  • Anatoly Anisimov
  • Andrii Nykonenko
  • Tetiana Rossada
  • Egor Melnikov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10333)

Abstract

A new effective algorithm and a system for paraphrase identification have been developed using a machine learning approach. The system architecture has the form of a multilayer classifier. According to their strategies, sub-classifiers of the lower level make decisions about the presence of paraphrase in sentences, while a super-classifier of the upper level makes the final decision. Conducted experiments demonstrated that the system has the accuracy of the paraphrase detection comparable with the best known analogous systems while being superior to all of them in implementation.

Keywords

Machine learning SVM Paraphrase identification 

Notes

Acknowledgments

The authors of the article are grateful to PHASE ONE: KARMA LTD. company, especially to the Unplag team for the support in research and considerable assistance in the development, testing and implementation of the paraphrase identification method.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Oleksandr Marchenko
    • 1
  • Anatoly Anisimov
    • 1
  • Andrii Nykonenko
    • 2
  • Tetiana Rossada
    • 1
  • Egor Melnikov
    • 3
  1. 1.Taras Shevchenko National University of KyivKyivUkraine
  2. 2.International Research and Training Center for IT and SystemsKyivUkraine
  3. 3.PHASE ONE: KARMA LTD.LondonUK

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