Exploring the Role of Gender in 19th Century Fiction Through the Lens of Word Embeddings

  • Siobhán GraysonEmail author
  • Maria Mulvany
  • Karen Wade
  • Gerardine Meaney
  • Derek Greene
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10318)


Within the last decade, substantial advances have been made in the field of computational linguistics, due in part to the evolution of word embedding algorithms inspired by neural network models. These algorithms attempt to derive a set of vectors which represent the vocabulary of a textual corpus in a new embedded space. This new representation can then be used to measure the underlying similarity between words. In this paper, we explore the role an author’s gender may play in the selection of words that they choose to construct their narratives. Using a curated corpus of forty-eight 19th century novels, we generate, visualise, and investigate word embedding representations using a list of gender-encoded words. This allows us to explore the different ways in which male and female authors of this corpus use terms relating to contemporary understandings of gender and gender roles.



This research was partly supported by Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289, in collaboration with the Nation, Genre and Gender project funded by the Irish Research Council.


  1. 1.
    Argamon, S., Koppel, M., Fine, J., Shimoni, A.R.: Gender, genre, and writing style in formal written texts. Text 23, 321–346 (2003)CrossRefGoogle Scholar
  2. 2.
    Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media, Inc., Sebastopol (2009)zbMATHGoogle Scholar
  3. 3.
    Bolukbasi, T., Chang, K.W., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In: Advances in Neural Information Processing Systems, pp. 4349–4357 (2016)Google Scholar
  4. 4.
    Firth, J.R.: A synopsis of linguistic theory 1930–55. In: Selected papers of J.R. Firth, 1952–59, pp. 1–32 (1957)Google Scholar
  5. 5.
    Grayson, S., Mulvany, M., Wade, K., Meaney, G., Greene, D.: Novel2Vec: characterising 19th century fiction via word embeddings. In: Proceedings of the 24 Irish AICS (2016)Google Scholar
  6. 6.
    Hamilton, W.L., Leskovec, J., Jurafsky, D.: Diachronic word embeddings reveal statistical laws of semantic change. In: Proceedings of the 54th ACL (2016)Google Scholar
  7. 7.
    Jockers, M.L.: Macroanalysis: Digital Methods and Literary History. University of Illinois Press, Urbana (2013)Google Scholar
  8. 8.
    Jockers, M.L., Mimno, D.: Significant themes in 19th-century literature. Poetics 41(6), 750–769 (2013)CrossRefGoogle Scholar
  9. 9.
    van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar
  10. 10.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of the Workshop on ICLR (2013)Google Scholar
  11. 11.
    Moretti, F.: Network theory, plot analysis. New Left Rev. 68, 80–102 (2011)Google Scholar
  12. 12.
    Reagan, A.J., Mitchell, L., Kiley, D., Danforth, C.M., Dodds, P.S.: The emotional arcs of stories are dominated by six basic shapes. arXiv e-prints (2016)Google Scholar
  13. 13.
    Schmidt, B.: Rejecting the gender binary: a vector-space operation (2015).

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Siobhán Grayson
    • 1
    Email author
  • Maria Mulvany
    • 2
  • Karen Wade
    • 2
  • Gerardine Meaney
    • 2
  • Derek Greene
    • 1
  1. 1.School of Computer ScienceUniversity College DublinDublinIreland
  2. 2.Humanities InstituteUniversity College DublinDublinIreland

Personalised recommendations