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Designing Close and Distant Reading Visualizations for Text Re-use

  • Stefan Jänicke
  • Thomas Efer
  • Marco Büchler
  • Gerik Scheuermann
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 550)

Abstract

We present various visualizations for the Text Re-use found among texts of a collection to support answering a broad palette of research questions in the humanities. When juxtaposing all texts of a corpus in form of tuples, we propose the Text Re-use Grid as a distant reading method that emphasizes text tuples with systematic or repetitive Text Re-use. The Text Re-use Browser provides a closer look on the Text Re-use between the two texts of a tuple. Additionally, we present Text Re-use Alignment Visualizations to improve the readability of Text Variant Graphs that are used to compare various text editions to each other. Finally, we illustrate the benefit of the proposed visualizations with four usage scenarios for various topics in literary criticism.

Keywords

Text Re-use Text visualization Text variant graph Literary criticism Digital humanities 

Notes

Acknowledgements

The authors like to thank Sarah Bowen (Aga Khan University), who utilized the presented Text Re-use Visualizations for historic Arabic texts, Eva Wöckener-Gade (Leipzig University), who worked with the Text Re-use Alignment Visualization to analyze the various meanings of ancient Greek terms and Annette Geßner (Göttingen Centre for Digital Humanities) for the collaboration when designing the Text Re-use Visualizations for English Bible translations. This research was funded by the German Federal Ministry of Education and Research.

References

  1. 1.
    Andrews, T.L., Macé, C.: Beyond the tree of texts: Building an empirical model of scribal variation through graph analysis of texts and stemmata. Literary and Linguistic Computing (2013)Google Scholar
  2. 2.
    Bourdaillet, J., Ganascia, J.G.: Practical block sequence alignment with moves. In: Loos, R., Fazekas, S. Z., Martn-Vide, C. (eds.) LATA. vol. Report 35/07, pp. 199–210. Research Group on Mathematical Linguistics, Universitat Rovira i Virgili, Tarragona (2007)Google Scholar
  3. 3.
    Büchler, M.: Informationstechnische Aspekte des Historical Text Re-use (2013)Google Scholar
  4. 4.
    Büchler, M., Geßner, A., Eckart, T., Heyer, G.: Unsupervised detection and visualisation of textual reuse on ancient Greek texts. J. Chicago Colloquium Digit. Humanit. Comput. Sci. 1(2) (2010). https://letterpress.uchicago.edu/index.php/jdhcs/article/view/60/71
  5. 5.
    Cheesman, T., Flanagan, K., Rybicki, J., Thiel, S.: Six maps of translations of Shakespeare. In: Wiggin, B., Macleod, C., DiMassa, D., Theis, N. (eds.) Un/Translatables: New Maps for Germanic Literatures. Northwestern University Press, Evanston (2014)Google Scholar
  6. 6.
    Clough, P., Gaizauskas, R., Piao, S.S.L., Wilks, Y.: METER: MEasuring TExt Reuse. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 152–159, ACL 2002. Association for Computational Linguistics, Stroudsburg, PA, USA (2002)Google Scholar
  7. 7.
    Collins, C., Carpendale, S., Penn, G.: Visualization of uncertainty in lattices to support decision-making. In: Proceedings of the 9th Joint Eurographics/IEEE VGTC Conference on Visualization, pp. 51–58, EUROVIS 2007. Eurographics Association, Aire-la-Ville, Switzerland (2007)Google Scholar
  8. 8.
    Dekker, R. H., Middell, G.: Computer-Supported Collation with CollateX: Managing Textual Variance in an Environment with Varying Requirements. Supporting Digital Humanities (2011)Google Scholar
  9. 9.
    Diehl, S.: Software Visualization: Visualizing the Structure, Behaviour, and Evolution of Software. Springer, Secaucus (2007)zbMATHGoogle Scholar
  10. 10.
    Efer, T., Heyer, G., Jost, J.: Text Mining am Beispiel der Dramen Shakespeares. In: Jansohn, C., (ed.) Proceedings of the Symposium “Shakespeare unter den Deutschen” (2014)Google Scholar
  11. 11.
    Gibbs, A.J., McIntyre, G.A.: The diagram, a method for comparing sequences. Its use with amino acid and nucleotide sequences. Eur. J. Biochem. 16(1), 1–11 (1970)CrossRefGoogle Scholar
  12. 12.
    GuttenPlag: GuttenPlag Wiki Visualizations (2013). http://de.guttenplag.wikia.com/wiki/Visualisierungen. Accessed 10 June 2013
  13. 13.
    Jänicke, S., Büchler, M., Scheuermann, G.: Improving the layout for text variant graphs. In: VisLR: Visualization as Added Value in the Development, Use and Evaluation of Language Resources, pp. 41–48 (2014)Google Scholar
  14. 14.
    Jenks, G.F., Caspall, F.C.: Error on choroplethic maps: definition, measurement, reduction. Ann. Assoc. Am. Geogr. 61(2), 217–244 (1971)CrossRefGoogle Scholar
  15. 15.
    John, M., Heimerl, F., Müller, A., Koch, S.: A visual focus+context approach for text comparison tasks. In: VisLR: Visualization as Added Value in the Development, Use and Evaluation of Language Resources, pp. 29–32 (2014)Google Scholar
  16. 16.
    Lee, J.: A computational model of text reuse in ancient literary texts. In: Association for Computational Linguistics, Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 472–479 (2007)Google Scholar
  17. 17.
    Moretti, F.: Distant Reading. Verso (2013)Google Scholar
  18. 18.
    Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)CrossRefGoogle Scholar
  19. 19.
    Nelson, T.H.: Xanalogical structure, needed now more than ever: parallel documents, deep links to content, deep versioning, and deep re-use. ACM Comput. Surv. (CSUR) 31(4es), 33 (1999)CrossRefGoogle Scholar
  20. 20.
    Ribler, R.L., Abrams, M.: Using visualization to detect plagiarism in computer science classes. In: Proceedings of the IEEE Symposium on Information Visualization, pp. 173–178, INFOVIS 2000. IEEE Computer Society, Washington, DC, USA (2000)Google Scholar
  21. 21.
    Schmidt, D., Colomb, R.: A data structure for representing multi-version texts online. Int. J. Hum.-Comput. Stud. 67(6), 497–514 (2009)CrossRefGoogle Scholar
  22. 22.
    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Visual Languages, Proceedings, pp. 336–343 (1996)Google Scholar
  23. 23.
    Slocum, T.A., McMaster, R.B., Kessler, F.C., Howard, H.H.: Thematic Cartography and Geovisualization. Prentice Hall Series in Geographic Information Science, 3, international edn. Prentice Hall, Englewood Cliffs (2009)Google Scholar
  24. 24.
    Ware, C.: Information Visualization: Perception for Design. Morgan Kaufmann Publishers Inc., San Francisco (2004)Google Scholar
  25. 25.
    Wattenberg, M., Viégas, F.B.: The word tree, an interactive visual concordance. IEEE Trans. Vis. Comput. Graph. 14(6), 1221–1228 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Stefan Jänicke
    • 1
  • Thomas Efer
    • 2
  • Marco Büchler
    • 3
  • Gerik Scheuermann
    • 1
  1. 1.Image and Signal Processing GroupLeipzig UniversityLeipzigGermany
  2. 2.Natural Language Processing GroupLeipzig UniversityLeipzigGermany
  3. 3.Göttingen Centre for Digital HumanitiesUniversity of GöttingenGöttingenGermany

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