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Experimental Comparison of Semantic Word Clouds

  • Lukas Barth
  • Stephen G. Kobourov
  • Sergey Pupyrev
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8504)

Abstract

We study the problem of computing semantics-preserving word clouds in which semantically related words are close to each other. We implement three earlier algorithms for creating word clouds and three new ones. We define several metrics for quantitative evaluation of the resulting layouts. Then the algorithms are compared according to these metrics, using two data sets of documents from Wikipedia and research papers. We show that two of our new algorithms outperform all the others by placing many more pairs of related words so that their bounding boxes are adjacent. Moreover, this improvement is not achieved at the expense of significantly worsened measurements for the other metrics.

Keywords

Edge Weight Semantic Relation Ranking Function Term Frequency Related Word 
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.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lukas Barth
    • 1
  • Stephen G. Kobourov
    • 2
  • Sergey Pupyrev
    • 2
    • 3
  1. 1.Institute of Theoretical InformaticsKarlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.Department of Computer ScienceUniversity of ArizonaTucsonUSA
  3. 3.Institute of Mathematics and Computer ScienceUral Federal UniversityEkaterinburgRussia

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