Establishing Relevance of Characteristic Features for Authorship Attribution with ANN

  • Urszula Stańczyk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8056)


Authorship attribution is perceived as a task of the paramount importance within stylometric analysis of texts. It encompasses author characterisation and comparison, and by observation and recognition of patterns in individual stylistic traits enables confirmation or rejection of authorship claims. Stylometry requires reliable textual descriptors and knowledge about their relevance for the case under study. One of the possible ways to evaluate this relevance is to employ a feature selection and reduction algorithm in the wrapper model. The paper presents research on such procedure applied to artificial neural networks used to categorise literary texts with respect to their authors, with importance of attributes discovered through sequential backward search.


Stylometry Authorship Attribution Characteristic Feature Feature Relevance Feature Selection Sequential Backward Search 


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  1. 1.
    Argamon, S., Burns, K., Dubnov, S. (eds.): The structure of style: Algorithmic approaches to understanding manner and meaning. Springer, Berlin (2010)Google Scholar
  2. 2.
    Burrows, J.: Textual analysis. In: Schreibman, S., Siemens, R., Unsworth, J. (eds.) A Companion to Digital Humanities. Blackwell, Oxford (2004)Google Scholar
  3. 3.
    Craig, H.: Stylistic analysis and authorship studies. In: Schreibman, S., Siemens, R., Unsworth, J. (eds.) A Companion to Digital Humanities. Blackwell, Oxford (2004)Google Scholar
  4. 4.
    Fiesler, E., Beale, R.: Handbook of neural computation. Oxford University Press (1997)Google Scholar
  5. 5.
    Jensen, R., Shen, Q.: Computational Intelligence and Feature Selection. John Wiley & Sons, Inc., Hoboken (2008)CrossRefGoogle Scholar
  6. 6.
    Kavzoglu, T., Mather, P.: Assessing artificial neural network pruning algorithms. In: Proceedings of the 24th Annual Conference and Exhibition of the Remote Sensing Society, Greenwich, UK, pp. 603–609 (2011)Google Scholar
  7. 7.
    Peng, R., Hengartner, H.: Quantitative analysis of literary styles. The American Statistician 56(3), 15–38 (2002)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Stańczyk, U.: Rough set-based analysis of characteristic features for ANN classifier. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds.) HAIS 2010, Part I. LNCS, vol. 6076, pp. 565–572. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Stańczyk, U.: Rough set and artificial neural network approach to computational stylistics. In: Ramanna, S., Howlett, R., Jain, L. (eds.) Emerging Paradigms in Machine Learning. SIST, vol. 13, pp. 441–470. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Waugh, S., Adams, A., Tweedie, F.: Computational stylistics using artificial neural networks. Literary and Linguistic Computing 15(2), 187–198 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Urszula Stańczyk
    • 1
  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

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