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International Conference on Applications of Natural Language to Information Systems

NLDB 2016: Natural Language Processing and Information Systems pp 421–426Cite as

Computer-Generated Text Detection Using Machine Learning: A Systematic Review

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9612))

Abstract

Computer-generated text or artificial text nowadays is in abundance on the web, ranging from basic random word salads to web scraping. In this paper, we present a short version of systematic review of some existing automated methods aimed at distinguishing natural texts from artificially generated ones. The methods were chosen by certain criteria. We further provide a summary of the methods considered. Comparisons, whenever possible, use common evaluation measures, and control for differences in experimental set-up.

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References

  1. Grechnikov, E.A., Gusev, G.G., Kustarev, A.A., Raigorodsky, A.M.: Detection of artificial texts, digital libraries: advanced methods and technologies, digital collections. In: Proceedings of XI All-Russian Research Conference RCDL 2009, KRC RAS, Petrozavodsk, pp. 306–308 (2009)

    Google Scholar 

  2. Corston-Oliver, S., Gamon, M., Brockett, C.: A machine learning approach to the automatic evaluation of machine translation. In: Proceeding of 39th Annual Meeting on Association for Computational Linguistics, ACL 2001, pp. 148–155 (2001)

    Google Scholar 

  3. Urvoy, T., Lavergne, T., Filoche, P.: Tracking web spam with hidden style similarity. In: AIRWEB 2006, Seattle, Washington, USA, 10 August 2006

    Google Scholar 

  4. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, Burlington (2011)

    Google Scholar 

  5. Arase, Y., Zhou, M.: Machine translation detection from monolingual web-text. In: Proceedings of 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria, pp. 1597–1607, 4–9 August 2013

    Google Scholar 

  6. Baayen, R.H.: Word Frequency Distributions. Kluwer Academic Publishers, Amsterdam (2001)

    Book  MATH  Google Scholar 

  7. Clarkson, P., Rosenfeld, R.: Statistical language modeling using the CMU-Cambridge toolkit. In: Proceedings of Eurospeech 1997, pp. 2707–2710 (1997)

    Google Scholar 

  8. Chickering, D.M., Heckerman, D., Meek, C.: A Bayesian approach to learning Bayesian networks with local structure. In: Geiger, D., Shenoy, P.P. (eds.) Proceedings of 13th Conference on Uncertainty in Artificial Intelligence, pp. 80–89 (1997)

    Google Scholar 

  9. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    Book  MATH  Google Scholar 

  10. Chen, S.F., Goodman, J.T.: An empirical study of smoothing techniques for language modeling. In: Proceedings of 34th Annual Meeting of the Association for Computational Linguistics (ACL), Santa Cruz, pp. 310–318 (1996)

    Google Scholar 

  11. Honore, A.: Some simple measures of richness of vocabulary. Assoc. Lit. Linguist. Comput. Bull. 7(2), 172–177 (1979)

    Google Scholar 

  12. Sichel, H.: On a distribution law for word frequencies. J. Am. Stat. Assoc. 70, 542–547 (1975)

    Google Scholar 

  13. Lavergne, T., Urvoy, T., Yvon, F.: Detecting fake content with relative entropy scoring. In: PAN 2008 (2008)

    Google Scholar 

  14. Seymore, K., Rosenfeld, R.: Scalable backoff language models. In: ICSLP 1996, Philadelphia, PA, vol. 1, pp. 232–235 (1996)

    Google Scholar 

  15. Stolcke, A.: Entropy-based pruning of backoff language models (1998)

    Google Scholar 

  16. Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  17. Gyongyi, Z., Garcia-Molina, H.: Web spam taxonomy. In: 1st International Workshop on Adversarial Information Retrieval on the Web (AIRWeb 2005) (2005)

    Google Scholar 

  18. Heymann, P., Koutrika, G., Garcia-Molina, H.: Fighting spam on social web sites: a survey of approaches and future challenges. IEEE Mag. Internet Comput. 11(6), 36–45 (2007)

    Article  Google Scholar 

  19. Labbé, C., Labbé, D.: Duplicate and fake publications in the scientific literature: how many SCIgen papers in computer science? Scientometrics, Akadémiai Kiadó, p. 10 (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Russian Academy of National Economy and Public Administration, Anti-Plagiat Research, Moscow, Russia

    Daria Beresneva

Authors
  1. Daria Beresneva
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Correspondence to Daria Beresneva .

Editor information

Editors and Affiliations

  1. ConservatoireNational desArts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Salford, Salford, United Kingdom

    Farid Meziane

  3. University of Salford, Salford, United Kingdom

    Mohamad Saraee

  4. Oakland University, Rochester, Michigan, USA

    Vijayan Sugumaran

  5. University of Salford, Salford, United Kingdom

    Sunil Vadera

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© 2016 Springer International Publishing Switzerland

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Beresneva, D. (2016). Computer-Generated Text Detection Using Machine Learning: A Systematic Review. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_43

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  • DOI: https://doi.org/10.1007/978-3-319-41754-7_43

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41753-0

  • Online ISBN: 978-3-319-41754-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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