Visualising Latent Semantic Spaces for Sense-Making of Natural Language Text

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10871)


Latent Semantic Analysis is widely used for natural language processing, but is difficult to visualise and interpret. We present an interactive visualisation that enables the interpretation of latent semantic spaces. It combines a multi-dimensional scatterplot diagram with a novel clutter-reduction strategy based on hierarchical clustering. A study with 12 non-expert participants showed that our visualisation was significantly more usable than experimental alternatives, and helped users make better sense of the latent space.


Latent Semantic Space Clutter Reduction Heatmap Matrix Word Cloud Post-Study System Usability Questionnaire (PSSUQ) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Process. 25(October), 259–284 (1998)CrossRefGoogle Scholar
  2. 2.
    Turney, P.D., Pantel, P.: From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 37, 141–188 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Landauer, T.K., Laham, D., Rehder, B., Schreiner, M.E.: How well can passage meaning be derived without using word order? A comparison of latent semantic analysis and humans. In: Proceedings of the Cognitive Science Society, pp. 412–417 (1997)Google Scholar
  4. 4.
    Landauer, T.K., Laham, D., Derr, M.: From paragraph to graph: latent semantic analysis for information visualization. Proc. NAS USA 101, 5214–5219 (2004)CrossRefGoogle Scholar
  5. 5.
    Habernal, I., Brychcín, T.: Semantic spaces for sentiment analysis. In: Habernal, I., Matoušek, V. (eds.) TSD 2013. LNCS (LNAI), vol. 8082, pp. 484–491. Springer, Heidelberg (2013). Scholar
  6. 6.
    Elmqvist, N., Dragicevic, P., Fekete, J.D.: Rolling the dice: multidimensional visual exploration using scatterplot matrix navigation. IEEE TVCG 14(6), 1141–1148 (2008)Google Scholar
  7. 7.
    Fua, Y.H., Ward, M.O., Rundensteiner, E.A.: Hierarchical parallel coordinates for exploration of large datasets. In: Proceedings of the Conference on Visualization, pp. 43–50 (1999)Google Scholar
  8. 8.
    Carr, D.B., Littlefield, R.J., Nicholson, W.L., Littlefield, J.S.: Scattterplot matrix techniques for large N. J. Am. Stat. Assoc. 82(398), 424–436 (1987)Google Scholar
  9. 9.
    Zhu, W., Chen, C.: Storylines: visual exploration and analysis in latent semantic spaces. Comput. Graph. 31, 338–349 (2007)CrossRefGoogle Scholar
  10. 10.
    Ellis, G., Dix, A.: A taxonomy of clutter reduction for information visualisation. IEEE Trans. Vis. Comput. Graph. 13(6), 1216–1223 (2007)CrossRefGoogle Scholar
  11. 11.
    Elmqvist, N., Fekete, J.D.: Hierarchical aggregation for information visualization: overview, techniques, and design guidelines. IEEE Trans. Vis. Comput. Graph. 16(3), 439–454 (2010)CrossRefGoogle Scholar
  12. 12.
    Shaoul, C., Westbury, C.: The Westbury Lab Wikipedia Corpus. University of Alberta, Edmonton (2010)Google Scholar
  13. 13.
    Rehurek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, Valletta, Malta, ELRA, pp. 45–50, May 2010Google Scholar
  14. 14.
    Baker, K.: Singular value decomposition tutorial. Ohio State Univ. 24, 16–23 (2005)Google Scholar
  15. 15.
    Landauer, T.K., McNamara, D.S., Dennis, S., Kintsch, W.: Handbook of Latent Semantic Analysis (2007)Google Scholar
  16. 16.
    Salvador, S., Chan, P.: Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms. In: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, pp. 576–584 (2004)Google Scholar
  17. 17.
    Pirolli, P., Card, S.: Information foraging. Psychol. Rev. 106(4), 643 (1999)CrossRefGoogle Scholar
  18. 18.
    Wang, M.P., Jones, M.C.: Comparison of smoothing parameterization in bivariate kernel density estimation. J. Am. Stat. Assoc. 88(422), 520–528 (1993)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Kurlander, D., Feiner, S.: Editable graphical histories. In: IEEE Visual Languages, pp. 127–134 (1988)Google Scholar
  20. 20.
    Cockburn, A., Karlson, A., Bederson, B.B.: A review of overview+detail, zooming, and focus+context interfaces. ACM Comput. Surv. 41, 1–31 (2008)CrossRefGoogle Scholar
  21. 21.
    Lewis, J.: IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use. IJHCI 7(1), 57–78 (1995)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer LaboratoryUniversity of CambridgeCambridgeUK
  2. 2.Microsoft ResearchCambridgeUK

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