Using Alliteration in Authorship Attribution of Historical Texts

  • Lubomir IvanovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9924)


The paper describes the use of alliteration, by itself or in combination with other features, in training machine learning algorithms to perform attribution of texts of unknown/disputed authorship. The methodology is applied to a corpus of 18th century political writings, and used to improve the attribution accuracy.


Authorship attribution Alliteration Lexical stress Machine learning 



We would like to acknowledge the help of Dr. S. Petrovic and G. Berton, and the assistance of S. Campbell, who carried out some of the weighted voting experiments.


  1. 1.
    Stamatatos, E.: A survey of modern authorship attribution methods. J. Am. Soc. Inform. Sci. Technol. 60(3), 538–556 (2009)CrossRefGoogle Scholar
  2. 2.
    Abbasi, A., Chen, H.: Applying authorship analysis to extremist-group web forum messages. IEEE Intell. Syst. 20(5), 67–75 (2005)CrossRefGoogle Scholar
  3. 3.
    Argamon, S., Saric, M., Stein, S.: Style mining of electronic messages for multiple authorship discrimination. In: Proceedings of the 9th ACM SIGKDD, pp. 475–480 (2003)Google Scholar
  4. 4.
    de Vel, O., Anderson, A., Corney, M., Mohay, G.M.: Mining e-mail content for author identification forensics. SIGMOD Rec. 30(4), 55–64 (2001)CrossRefGoogle Scholar
  5. 5.
    Zheng, R., Li, J., Chen, H., Huang, Z.: A framework for authorship identification of online messages: writing style features and classification techniques. J. Am. Soc. Inf. Sci. Technol. 57(3), 378–393 (2006)CrossRefGoogle Scholar
  6. 6.
    Mosteller, F., Wallace, D.: Inference and Disputed Authorship: The Federalist. Addison-Wesley, Reading (1964)zbMATHGoogle Scholar
  7. 7.
    Lowe, D., Matthews, R.: Shakespeare vs. Fletcher: a stylometric analysis by radial basis functions. Comput. Humanit. 29, 449–461 (1995)CrossRefGoogle Scholar
  8. 8.
    Matthews, R., Merriam, T.: Neural computation in stylometry: an application to the works of Shakespeare and Fletcher. Literary Linguist. Comput. 8(4), 203–209 (1993)CrossRefGoogle Scholar
  9. 9.
    Burrows, J.: Computation into Criticism: A Study of Jane Austen’s Novels and an Experiment in Method. Clarendon Press, Oxford (1987)Google Scholar
  10. 10.
    Morton, A.Q.: The Authorship of Greek Prose. J. Roy. Stat. Soc. (A) 128, 169–233 (1965)CrossRefGoogle Scholar
  11. 11.
    Petrovic, S., Berton, G., Campbell, S., Ivanov, L.: Attribution of 18th century political writings using machine learning. J. Technol. Soc. 11(3), 1–13 (2015)CrossRefGoogle Scholar
  12. 12.
    Ivanov, L., Petrovic, S.: Using lexical stress in authorship attribution of historical texts. In: Král, P., Matoušek, V. (eds.) TSD 2015. LNCS, vol. 9302, pp. 105–113. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24033-6_12 CrossRefGoogle Scholar
  13. 13.
    Petrovic, S., Berton, G., Schiaffino, R., Ivanov, L.: Authorship attribution of Thomas Paine works. In: Proceedings of the International Conference on Data Mining, DMIN 2014, 12–24 July 2014, pp. 182–188. CSREA Press, ISBN: 1- 60132-267-4Google Scholar
  14. 14.
    Kricka, L.: Alliteration, Again and Again. Xlibris Publication (2013). ISBN: 978-1479776467.
  15. 15.
  16. 16.
  17. 17.
  18. 18.
    Barquist, C., Shie, D.: Computer analysis of alliteration in beowulf using distinctive feature theory. Lit Linguist Comput. 6(4), 274–280 (1991). doi: 10.1093/llc/6.4.274 CrossRefGoogle Scholar
  19. 19.
    Kotzé, E.: Author identification from opposing perspectives in forensic linguistics. South. Afr. Linguist. Appl. Lang. Stud. 28(2), 185–197 (2010). CrossRefGoogle Scholar
  20. 20.
  21. 21.
    Angelou, M.: The Collected Autobiographies of Maya. Modern Library, New York (2004). ISBN: 978-0679643258Google Scholar
  22. 22.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, L.: The WEKA data mining software: an update. In: ACM SIGKDD Explorations Newsletter, vol. 11(1), pp. 10–18. ACM, New York (2009). doi: 10.1145/1656274.1656278
  23. 23.
    Juola, P.: Authorship attribution. Found. Trends Inf. Retrieval 3, 233–334 (2006)Google Scholar
  24. 24.
    Toutanova, K., Klein, D., Manning, C.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: HLT-NAACL, pp. 252–259 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Computer Science DepartmentIona CollegeNew RochelleUSA

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