The Use of Orthogonal Similarity Relations in the Prediction of Authorship

  • Upendra Sapkota
  • Thamar Solorio
  • Manuel Montes-y-Gómez
  • Paolo Rosso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7817)


Recent work on Authorship Attribution (AA) proposes the use of meta characteristics to train author models. The meta characteristics are orthogonal sets of similarity relations between the features from the different candidate authors. In that approach, the features are grouped and processed separately according to the type of information they encode, the so called linguistic modalities. For instance, the syntactic, stylistic and semantic features are each considered different modalities as they represent different aspects of the texts. The assumption is that the independent extraction of meta characteristics results in more informative feature vectors, that in turn result in higher accuracies. In this paper we set out to the task of studying the empirical value of this modality specific process. We experimented with different ways of generating the meta characteristics on different data sets with different numbers of authors and genres. Our results show that by extracting the meta characteristics from splitting features by their linguistic dimension we achieve consistent improvement of prediction accuracy.


Feature Vector Computational Linguistics Punctuation Mark Stylistic Feature Meta Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Baker, L.D., McCallum, A.: Distributional clustering of words for text classification. In: SIGIR 1998: Proceedings of the 21st Annual International ACM SIGIR, pp. 96–103. ACM, Melbourne (1998)Google Scholar
  2. 2.
    Biber, D.: The multi-dimensional approach to linguistic analyses of genre variation: An overview of methodology and findings. Computers and the Humanities 26, 331–345 (1993)Google Scholar
  3. 3.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the 1998 Conference on Computational Learning Theory (1998)Google Scholar
  4. 4.
    Dhillon, I.S., Mallela, S., Kumar, R.: A divisive information-theoretic feature clsutering algorithm for text classification. Journal of Machine Learning Research 3, 1265–1287 (2003)zbMATHGoogle Scholar
  5. 5.
    Escalante, H.J., Montes-y-Gómez, M., Solorio, T.: A weighted profile intersection measure for profile-based authorship attribution. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011, Part I. LNCS, vol. 7094, pp. 232–243. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  6. 6.
    Escalante, H.J., Solorio, T., Montes-y-Gomez, 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, pp. 288–298. Association for Computational Linguistics, Portland (2011)Google Scholar
  7. 7.
    Hayes, J.H.: Authorship attribution: A principal component and linear discriminant analysis of the consistent programmer hypothesis. I. J. Comput. Appl., 79–99 (2008)Google Scholar
  8. 8.
    Houvardas, J., Stamatatos, E.: N-gram feature selection for authorship identification. In: Euzenat, J., Domingue, J. (eds.) AIMSA 2006. LNCS (LNAI), vol. 4183, pp. 77–86. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Karypis, G.: CLUTO - a clustering toolkit. Tech. Rep. #02-017 (November 2003)Google Scholar
  10. 10.
    Keselj, V., Peng, F., Cercone, N., Thomas, C.: N-gram based author profiles for authorship attribution. In: Proceedings of the Pacific Association for Computational Linguistics, pp. 255–264 (2003)Google Scholar
  11. 11.
    Koppel, M., Schler, J., Argamon, S.: Authorship attribution in the wild. Language Resources and Evaluation 45, 83–94 (2011)CrossRefGoogle Scholar
  12. 12.
    Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: A new benchmark collection for text categorization research. Journal of Machine Learning Research 5, 361–397 (2004)Google Scholar
  13. 13.
    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 2008), Manchester, UK, pp. 513–520 (August 2008)Google Scholar
  14. 14.
    Luyckx, K., Daelemans, W.: The effect of author set size and data size in authorship attribution. In: Literary and Linguistic Computing, pp. 1–21 (August 2010)Google Scholar
  15. 15.
    Marneffe, M.D., MacCartney, B., Manning, C.D.: Generating typed dependency parses from phrase structure parses. In: LREC 2006 (2006)Google Scholar
  16. 16.
    Plakias, S., Stamatatos, E.: Tensor space models for authorship identification. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds.) SETN 2008. LNCS (LNAI), vol. 5138, pp. 239–249. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Raghavan, S., Kovashka, A., Mooney, R.: Authorship attribution using probabilistic context-free grammars. In: Proceedings of the ACL 2010 Conference Short Papers, pp. 38–42. Association for Computational Linguistics, Uppsala (2010)Google Scholar
  18. 18.
    Slonim, N., Tishby, N.: The power of word clusters for text classification. In: 23rd European Colloquium on Information Retrieval Research, ECIR (2001)Google Scholar
  19. 19.
    Solorio, T., Pillay, S., Raghavan, S., Montes-y-Gómez: Generating metafeatures for authorship attribution on web forum posts. In: Proceedings of the 5th International Joint Conference on Natural Language Processing, IJCNLP 2011, pp. 156–164. AFNLP, Chiang Mai (2011)Google Scholar
  20. 20.
    Stamatatos, E.: Author identification using imbalanced and limited training texts. In: 18th International Workshop on Database and Expert Systems Applications, DEXA 2007, pp. 237–241 (September 2007)Google Scholar
  21. 21.
    Stamatatos, E.: Author identification: Using text sampling to handle the class imbalance problem. Information Processing and Managemement 44, 790–799 (2008)CrossRefGoogle Scholar
  22. 22.
    Stamatatos, E.: Plagiarism detection using stopword n-grams. Journal of the American Society for Information Science and Technology 62(12), 2512–2527 (2011)CrossRefGoogle Scholar
  23. 23.
    Stamatatos, E.: A survey on modern authorship attribution methods. Journal of the American Society for Information Science and Technology 60(3), 538–556 (2009)CrossRefGoogle Scholar
  24. 24.
    Stolcke, A.: SRILM - an extensible language modeling toolkit, pp. 901–904 (2002)Google Scholar
  25. 25.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, NAACL 2003, vol. 1, pp. 173–180 (2003)Google Scholar
  26. 26.
    de Vel, O., Anderson, A., Corney, M., Mohay, G.: Multi-topic e-mail authorship attribution forensics. In: Proceedings of the Workshop on Data Mining for Security Applications, 8th ACM Conference on Computer Security (2001)Google Scholar
  27. 27.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Upendra Sapkota
    • 1
  • Thamar Solorio
    • 1
  • Manuel Montes-y-Gómez
    • 2
  • Paolo Rosso
    • 3
  1. 1.University of Alabama at BirminghamBirminghamUSA
  2. 2.Instituto Nacional de Astrofísica, Optica y ElectrónicaPueblaMexico
  3. 3.NLE Lab - ELiRFUniversitat Politècnica de ValènciaValenciaSpain

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