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A data-driven computational semiotics: The semantic vector space of Magritte’s artworks

  • Jean-François Chartier EMAIL logo , Davide Pulizzotto , Louis Chartrand and Jean-Guy Meunier
Published/Copyright: September 5, 2019

Abstract

The rise of big digital data is changing the framework within which linguists, sociologists, anthropologists, and other researchers are working. Semiotics is not spared by this paradigm shift. A data-driven computational semiotics is the study with an intensive use of computational methods of patterns in human-created contents related to semiotic phenomena. One of the most promising frameworks in this research program is the Semantic Vector Space (SVS) models and their methods. The objective of this article is to contribute to the exploration of the SVS for a computational semiotics by showing what types of semiotic analysis can be accomplished within this framework. The study is applied to a unique body of digitized artworks. We conducted three short experiments in which we explore three types of semiotic analysis: paradigmatic analysis, componential analysis, and topic modelling analysis. The results reported show that the SVS constitutes a powerful framework within which various types of semiotic analysis can be carried out.

Appendix: Five nearest artworks of each topic in the semantic vector space

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Published Online: 2019-09-05
Published in Print: 2019-10-25

© 2019 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Introduction to Meaningful data/Données signifiantes
  3. A data-driven computational semiotics: The semantic vector space of Magritte’s artworks
  4. New approaches to plastic language: Prolegomena to a computer-aided approach to pictorial semiotics
  5. Mesures et savoirs : Quelles méthodes pour l’histoire culturelle à l’heure du big data ?
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  10. Differential heterogenesis and the emergence of semiotic function
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