Document Open Access Logo

The Love Equation: Computational Modeling of Romantic Relationships in French Classical Drama

Authors Folgert Karsdorp, Mike Kestemont, Christof Schöch, Antal van den Bosch

PDF
Thumbnail PDF

File

OASIcs.CMN.2015.98.pdf
  • Filesize: 496 kB
  • 10 pages

Document Identifiers

Author Details

Folgert Karsdorp
Mike Kestemont
Christof Schöch
Antal van den Bosch

Cite As Get BibTex

Folgert Karsdorp, Mike Kestemont, Christof Schöch, and Antal van den Bosch. The Love Equation: Computational Modeling of Romantic Relationships in French Classical Drama. In 6th Workshop on Computational Models of Narrative (CMN 2015). Open Access Series in Informatics (OASIcs), Volume 45, pp. 98-107, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015) https://doi.org/10.4230/OASIcs.CMN.2015.98

Abstract

We report on building a computational model of romantic relationships in a corpus of historical literary texts. We frame this task as a ranking problem in which, for a given character, we try to assign the highest rank to the character with whom (s)he is most likely to be romantically involved. As data we use a publicly available corpus of French 17th and 18th century plays (http://www.theatre-classique.fr/) which is well suited for this type of analysis because of the rich markup it provides (e.g. indications of characters speaking). We focus on distributional, so-called second-order features, which capture how speakers are contextually embedded in the texts. At a mean reciprocal rate (MRR) of 0.9 and MRR@1 of 0.81, our results are encouraging, suggesting that this approach might be successfully extended to other forms of social interactions in literature, such as antagonism or social power relations.

Subject Classification

Keywords
  • French drama
  • social relations
  • neural network
  • representation learning

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Apoorv Agarwal, Augusto Corvalan, Jacob Jensen, and Owen Rambow. Social network analysis of alice in wonderland. In The proceedings of Workshop on Computational Linguistics for Literature, NAACL 2012, pages 88-96, Montréal, Canada, 2012. Google Scholar
  2. Apoorv Agarwal, Anup Kotalwar, and Owen Rambow. Automatic extraction of social networks from literary text: A case study on alice in wonderland. In Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP 2013), pages 1202–-1208, Nagoya, Japan, 2013. Google Scholar
  3. Ricardo Alberich, Joe Miro-Julia, and Francesc Rosselló. Marvel universe looks almost like a real social network. Preprint, arXiv id 0202174, 2002. Google Scholar
  4. Mariona Coll Ardanuy and Caroline Sporleder. Structure-based clustering of novels. In Proceedings of the 3rd Workshop on Computational Linguistics for Literature (CLFL), pages 31-39, Gothenburg, Sweden, April 2014. Association for Computational Linguistics. Google Scholar
  5. David Bamman, Brendan O'Connor, and Noah Smith. Learning latent personas of film characters. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 352–-361, Sofia, Bulgaria, 2013. Google Scholar
  6. John Burrows. Computation into criticism. A Study of Jane Austen’s novels and an experiment in methods. Clarendon Press, 1987. Google Scholar
  7. Asli Celikyilmaz, Dilek Hakkani-Tur, Hua He, Greg Kondrak, and Denilson Barbosa. The actor-topic model for extracting social networks in literary narrative. In NIPS Workshop: Machine Learning for Social Computing, 2010. Google Scholar
  8. TEI Consortium. TEI P5: Guidelines for Electronic Text Encoding and Interchange. TEI Consortium, 2014. Google Scholar
  9. David K. Elson, Nicholas Dames, and Kathleen R. McKeown. Extracting social networks from literary fiction. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 138-147, Uppsala, Sweden, 2010. Google Scholar
  10. Paul Fievre, editor. Théâtre classique. Université Paris-IV Sorbonne, 2007-2014. Google Scholar
  11. Donald Ervin Knuth. The Stanford GraphBase: a platform for combinatorial computing. ACM Press Series. ACM Press, 1993. Google Scholar
  12. Quoc V. Le and Tomas Mikolov. Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014. Google Scholar
  13. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. In Proceedings of Workshop at ICLR, 2013. Google Scholar
  14. Franco Moretti. Network theory, plot analysis. New Left Review, 68:80-102, 2011. Google Scholar
  15. Mark Newman. Networks. An Introduction. Oxford University Press, New York, NY, USA, 2010. Google Scholar
  16. Gabriel Recchia, Alexandra L. Slater, and Max M. Louwerse. Predicting the good guy and the bad guy: Attitudes are encoded in language statistics. In Proceedings of the 36th Annual Conference of the Cognitive Science Society, pages 1264-1269, 2014. Google Scholar
  17. Susan Schreibman and Ray Siemens, editors. A Companion to Digital Literary Studies. Oxford: Blackwell, 2008. Google Scholar
  18. D. Sculley. Large scale learning to rank. In NIPS Workshop on Advances in Ranking, pages 58-63, 2009. Google Scholar
  19. Anne Ubersfeld, Frank Collins, Paul Perron, and Patrick Debbèche. Reading Theatre. Toronto Studies in Semiotics and Communication Series. University of Toronto Press, 1999. Google Scholar
  20. Matje Van de Camp and Antal Van den Bosch. The socialist network. Decision Support Systems, 53(4):761-769, 2012. Google Scholar
  21. Ellen M Voorhees and Dawn M Tice. The TREC-8 question answering track evaluation. In Proceedings of the Eighth Text REtrieval Conference (TREC 8), volume 1999, page 82, 1999. Google Scholar
  22. Scott Weingart. Demystifying networks, parts I & II. Journal of Digital Humanities, 1(1):9-21, 2012. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact