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.
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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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Marchenko, O., Anisimov, A., Nykonenko, A., Rossada, T., Melnikov, E. (2017). Machine Learning Method for Paraphrase Identification. In: Christiansen, H., Jaudoin, H., Chountas, P., Andreasen, T., Legind Larsen, H. (eds) Flexible Query Answering Systems. FQAS 2017. Lecture Notes in Computer Science(), vol 10333. Springer, Cham. https://doi.org/10.1007/978-3-319-59692-1_14
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DOI: https://doi.org/10.1007/978-3-319-59692-1_14
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