D02 | Evaluation Metrics for Visual Analytics in Linguistics

Prof. Miriam Butt, Universität Konstanz
Email | Website

Miriam Butt

Prof. Thomas Ertl, Universität Stuttgart
Email | Website

Thomas Ertl

Christin Schätzle, Universität Konstanz – Website

Within linguistics, the use of large sets of data via a combination of rule-based and stochastic methods is now standardly part of the analysis of language structure. However, though scatter plots, bar or pie charts, and trees as provided by R, for example, are standardly used, novel visual computation techniques have only just begun to be explored. The overall aim of this project is to evaluate whether visual analytics indeed represents a methodology that can yield improved results for linguistic research and to establish metrics for the evaluation of visual analytics approaches by conducting linguistically motivated case studies on historical data.

Research Questions

What visual variables and representations are most effective for which problems?

Which metrics for evaluation can be established?

What visual variables and representations are most effective for which problems?

Can visual analytic methods yield improved results within linguistic research?

Can we find linguistic patterns/insights we could not have found without visual analytics?

Can we find patterns/insights more quickly with visual analytics than without?

Glyph visualization of dative subjects and semantic verb classes in Icelandic.

Publications

  1. C. Schätzle, M. Hund, F. L. Dennig, M. Butt, and D. A. Keim, “HistoBankVis: Detecting Language Change via Data Visualization.,” in Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language, 2017, no. 133, pp. 32–39.
  2. C. Schätzle and D. Sacha, “Visualizing Language Change: Dative Subjects in Icelandic,” in Proceedings of the Language Resources and Evaluation Conference 2016 (Workshop “VisLRII: Visualization as Added Value in the Development, Use and Evaluation of Language Resources,” 2016, pp. 8–15.
  3. C. Schätzle, M. Hund, F. L. Dennig, M. Butt, and D. A. Keim, HistoBankVis: Detecting Language Change via Data Visualization, vol. Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language (NEALT Proceedings Series 32). 2017.
  4. A. Hautli-Janisz and V. Lyding, “VisLR II: Visualization as Added Value in the Development, Use and Evaluation of Language Resources,” in Proceedings of the Language Resources and Evaluation Conference 2016 (Workshop “VisLRII: Visualization as Added Value in the Development, Use and Evaluation of Language Resources,” 2016, pp. 8–15.
  5. C. Schulz, A. Nocaj, M. El-Assady, S. Frey, M. Hlawatsch, M. Hund, G. K. Karch, R. Netzel, C. Schätzle, M. Butt, D. A. Keim, T. Ertl, U. Brandes, and D. Weiskopf, “Generative Data Models for Validation and Evaluation of Visualization Techniques.,” in BELIV Workshop 2016, 2016, pp. 112–124.

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