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Using Explicit Semantic Analysis and Word2Vec in Measuring Semantic Relatedness of Russian Paraphrases

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Book cover Digital Transformation and Global Society (DTGS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 859))

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Abstract

In this study we compare two semantic relatedness algorithms, namely, Explicit Semantic Analysis (ESA) and Word2Vec. ESA represents text meaning in a high-dimensional space of concepts derived from Wikipedia. Word2Vec generates distributed vector representations from large text corpora). Experiments were carried out on the Russian paraphrase corpus of news titles and Russian ParaPlag paraphrase corpus. The paper contains thorough analysis of results and evaluation procedure.

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Notes

  1. 1.

    https://metacpan.org/pod/Text::Similarity.

  2. 2.

    https://en.wikipedia.org/wiki/SemEval.

  3. 3.

    http://wn-similarity.sourceforge.net/.

  4. 4.

    http://community.nzdl.org/ELKB/.

  5. 5.

    https://github.com/dkpro/dkpro-similarity.

  6. 6.

    https://github.com/faraday/wikiprep-esa.

  7. 7.

    http://treo.deri.ie/easyesa/.

  8. 8.

    https://code.google.com/archive/p/research-esa/.

  9. 9.

    https://github.com/ticcky/esalib.

  10. 10.

    https://github.com/pvoosten/explicit-semantic-analysis.

  11. 11.

    https://github.com/fozziethebeat/S-Space/.

  12. 12.

    https://github.com/fozziethebeat/S-Space/blob/master/src/main/java/edu/ucla/sspace/esa/ExplicitSemanticAnalysis.java .

  13. 13.

    https://radimrehurek.com/gensim/models/word2vec.html.

  14. 14.

    https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/examples/tutorials/word2vec/word2vec_basic.py.

  15. 15.

    https://github.com/deeplearning4j/deeplearning4j.

  16. 16.

    http://rusvectores.org/ru/.

  17. 17.

    http://pan.webis.de/tasks.html.

  18. 18.

    https://plagevalrus.github.io/.

  19. 19.

    http://ru-eval.ru/plageval/rules.html.

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Acknowledgements

The authors express their gratitude to anonymous reviewers for their careful reading of the paper, for their critical comments and for giving useful suggestions that helped to improve the work.

The research discussed in the paper is supported by the RFBR grant № 16-06-00529 «Development of a linguistic toolkit for semantic analysis of Russian text corpora by statistical techniques».

Author information

Authors and Affiliations

  1. Saint Petersburg State University, Universitetskaya emb 11, St. Petersburg, 199034, Russia

    Anna Kriukova, Olga Mitrofanova & Natalia Roschina

  2. Saint Petersburg Electrotechnical University «LETI», Popova str., 5, St. Petersburg, 197376, Russia

    Kirill Sukharev

Authors
  1. Anna Kriukova
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  2. Olga Mitrofanova
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  3. Kirill Sukharev
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  4. Natalia Roschina
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Corresponding author

Correspondence to Anna Kriukova .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, St. Petersburg, Russia

    Daniel A. Alexandrov

  2. Saint Petersburg State University of Information Technologies, St. Petersburg, Russia

    Alexander V. Boukhanovsky

  3. Saint Petersburg State University of Information Technologies, St. Petersburg, Russia

    Andrei V. Chugunov

  4. National Research University Higher School of Economics, St. Petersburg, Russia

    Yury Kabanov

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

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Kriukova, A., Mitrofanova, O., Sukharev, K., Roschina, N. (2018). Using Explicit Semantic Analysis and Word2Vec in Measuring Semantic Relatedness of Russian Paraphrases. In: Alexandrov, D., Boukhanovsky, A., Chugunov, A., Kabanov, Y., Koltsova, O. (eds) Digital Transformation and Global Society. DTGS 2018. Communications in Computer and Information Science, vol 859. Springer, Cham. https://doi.org/10.1007/978-3-030-02846-6_28

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  • DOI: https://doi.org/10.1007/978-3-030-02846-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02845-9

  • Online ISBN: 978-3-030-02846-6

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