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Improving Cross-Topic Authorship Attribution: The Role of Pre-Processing

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Computational Linguistics and Intelligent Text Processing (CICLing 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10762))

Abstract

The effectiveness of character n-gram features for representing the stylistic properties of a text has been demonstrated in various independent Authorship Attribution (AA) studies. Moreover, it has been shown that some categories of character n-grams perform better than others both under single and cross-topic AA conditions. In this work, we present an improved algorithm for cross-topic AA. We demonstrate that the effectiveness of character n-grams representation can be significantly enhanced by performing simple pre-processing steps and appropriately tuning the number of features, especially in cross-topic conditions.

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Notes

  1. 1.

    When large sets of HFWs are replaced by distinct symbols, the size of feature set increases.

  2. 2.

    http://www.nltk.org [last access: 12.01.2017].

  3. 3.

    We also examined naive Bayes classifier, which produced worse results but similar behaviour (not shown).

References

  1. Stamatatos, E.: A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol. 60, 538–556 (2009)

    Article  Google Scholar 

  2. Abbasi, A., Chen, H.: Applying authorship analysis to extremist-group Web forum messages. IEEE Intell. Syst. 20, 67–75 (2005)

    Article  Google Scholar 

  3. Chaski, C.E.: Who’s at the keyboard? Authorship attribution in digital evidence investigations. Int. J. Digit. Evid. 4, 1–13 (2005)

    Google Scholar 

  4. Coulthard, M.: On admissible linguistic evidence. J. Law Policy 21, 441–466 (2013)

    Google Scholar 

  5. Koppel, M., Seidman, S.: Automatically identifying pseudepigraphic texts. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, (EMNLP’13), pp. 1449–1454 (2013)

    Google Scholar 

  6. Stamatatos, E.: On the robustness of authorship attribution based on character n-gram features. J. Law Policy 21, 427–439 (2013)

    Google Scholar 

  7. Luyckx, K., Daelemans, W.: Authorship attribution and verification with many authors and limited data. In: Proceedings of the 22nd International Conference on Computational Linguistics (COLING’08), pp. 513–520 (2008)

    Google Scholar 

  8. Houvardas, J., Stamatatos, E.: N-gram feature selection for authorship identification. In: Proceedings of Artificial Intelligence: Methodologies, Systems, and Applications (AIMSA’06), pp. 77–86 (2006)

    Chapter  Google Scholar 

  9. Kestemont, M.: Function words in authorship attribution. From black magic to theory? In: Proceedings of the 3rd Workshop on Computational Linguistics for Literature (EACL’14), pp. 59–66 (2014)

    Google Scholar 

  10. Daelemans, W.: Explanation in computational stylometry. In: Proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing’13), pp. 451–462 (2013)

    Chapter  Google Scholar 

  11. Sapkota, U., Bethard, S., Montes-y-Gómez, M., Solorio, T.: Not all character n-grams are created equal: a study in authorship attribution. In: Proceedings of the 2015 Annual Conference of the North American Chapter of the ACL: Human Language Technologies (NAACL-HLT’15), pp. 93–102 (2015)

    Google Scholar 

  12. Hedegaard, S., Simonsen, J.G.: Lost in translation: authorship attribution using frame semantics. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (HLT’11), pp. 65–70 (2011)

    Google Scholar 

  13. Schwartz, R., Tsur, O., Rappoport, A., Koppel, M.: Authorship attribution of micro-messages. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP’13), pp. 1880–1891 (2013)

    Google Scholar 

  14. Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., Chanona-Hernández, L.: Syntactic n-grams as machine learning features for natural language processing. Expert Syst. Appl. 41, 853–860 (2014)

    Article  Google Scholar 

  15. Gómez-Adorno, H., Sidorov, G., Pinto, D., Markov, I.: A graph based authorship identification approach. In: Working Notes Papers of the CLEF 2015 Evaluation Labs (CLEF’15), vol. 1391. CEUR (2015)

    Google Scholar 

  16. Grieve, J.: Quantitative authorship attribution: an evaluation of techniques. Lit. Linguist. Comput. 22, 251–270 (2007)

    Article  Google Scholar 

  17. Stamatatos, E.: Author identification using imbalanced and limited training texts. In: Proceedings of the 18th International Conference on Database and Expert Systems Applications (DEXA’07), pp. 237–241 (2007)

    Google Scholar 

  18. Escalante, H.J., Solorio, T., Montes-y-Gómez, M.: Local histograms of character n-grams for authorship attribution. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (HLT’11), pp. 288–298 (2011)

    Google Scholar 

  19. Sapkota, U., Solorio, T., Montes-y-Gómez, M., Bethard, S., Rosso, P.: Cross-topic authorship attribution: will out-of-topic data help? In: Proceedings of the 25th International Conference on Computational Linguistics (COLING’14), pp. 1228–1237 (2014)

    Google Scholar 

  20. Khmelev, D.V., Teahan, W.J.: A repetition based measure for verification of text collections and for text categorization. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’03), pp. 104–110 (2003)

    Google Scholar 

  21. Marton, Y., Wu, N., Hellerstein, L.: On compression-based text classification. In: Proceedings of the 27th European conference on Advances in Information Retrieval Research (ECIR’05), pp. 300–314 (2005)

    Google Scholar 

  22. Peng, F., Schuurmans, D., Keselj, V., Wang, S.: Language independent authorship attribution with character level n-grams. In: Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics (EACL’03), pp. 267–274 (2003)

    Google Scholar 

  23. Qian, T., Liu, B., Chen, L., Peng, Z.: Tri-training for authorship attribution with limited training data. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL’14), pp. 345–351 (2014)

    Google Scholar 

  24. Stamatatos, E., Fakotakis, N., Kokkinakis, G.: Automatic text categorization in terms of genre and author. Comput. Linguist. 26, 471–495 (2000)

    Article  Google Scholar 

  25. de Vel, O.Y., Anderson, A., Corney, M., Mohay, G.M.: Mining email content for author identification forensics. SIGMOD Rec. 30, 55–64 (2001)

    Article  Google Scholar 

  26. Koppel, M., Schler, J., Bonchek-Dokow, E.: Measuring differentiability: unmasking pseudonymous authors. J. Mach. Learn. Res. 8, 1261–1276 (2007)

    MATH  Google Scholar 

  27. Stamatatos, E., Daelemans, W., Verhoeven, B., Juola, P., López-López, A., Potthast, M., Stein, B.: Overview of the author identification task at PAN 2015. In: Working Notes of CLEF 2015-Conference and Labs of the Evaluation Forum (2015)

    Google Scholar 

  28. Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: RCV1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)

    Google Scholar 

  29. Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), pp. 363–370 (2005)

    Google Scholar 

  30. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11, 10–18 (2009)

    Article  Google Scholar 

  31. Gómez-Adorno, H., Markov, I., Sidorov, G., Posadas-Durán, J., Sanchez-Perez, M.A., Chanona-Hernandez, L.: Improving feature representation based on a neural network for author profiling in social media texts. Comput. Intell. Neurosci. 2016, 13 (2016)

    Google Scholar 

  32. Kibriya, A.M., Frank, E., Pfahringer, B., Holmes, G.: Multinomial naive Bayes for text categorization revisited. In: Proceedings of the 17th Australian Joint Conference on Advances in Artificial Intelligence (AI’04), pp. 488–499 (2005)

    Google Scholar 

  33. Sidorov, G., Gómez-Adorno, H., Markov, I., Pinto, D., Loya, N.: Computing text similarity using tree edit distance. In: Proceedings of the Annual Conference of the North American Fuzzy Information processing Society (NAFIPS’15) and 5th World Conference on Soft Computing, pp. 1–4 (2015)

    Google Scholar 

  34. Markov, I., Gómez-Adorno, H., Sidorov, G., Gelbukh, A.: Adapting cross-genre author profiling to language and corpus. In: Working Notes Papers of the CLEF 2016 Evaluation Labs. CEUR Workshop Proceedings, vol. 1609, pp. 947–955. CLEF and CEUR-WS.org (2016)

    Google Scholar 

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Acknowledgments

This work was partially supported by the Mexican Government (CONACYT projects 240844, SNI, COFAA-IPN, SIP-IPN 20161947, 20161958, 20162204, 20162064, 20171813, 20171344, and 20172008).

Author information

Authors and Affiliations

  1. Center for Computing Research (CIC), Instituto Politécnico Nacional (IPN), Mexico City, Mexico

    Ilia Markov & Grigori Sidorov

  2. Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, Samos, Greece

    Efstathios Stamatatos

Authors
  1. Ilia Markov
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  2. Efstathios Stamatatos
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  3. Grigori Sidorov
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Corresponding author

Correspondence to Ilia Markov .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Markov, I., Stamatatos, E., Sidorov, G. (2018). Improving Cross-Topic Authorship Attribution: The Role of Pre-Processing. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_21

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  • DOI: https://doi.org/10.1007/978-3-319-77116-8_21

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-77116-8

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