A Comparison of Unsupervised Methods to Associate Colors with Words

  • Gözde Özbal
  • Carlo Strapparava
  • Rada Mihalcea
  • Daniele Pighin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6975)


Colors have a very important role on our perception of the world. We often associate colors with various concepts at different levels of consciousnes and these associations can be relevant to many fields such as education and advertisement. However, to the best of our knowledge, there are no systematic approaches to aid the automatic development of resources encoding this kind of knowledge. In this paper, we propose three computational methods based on image analysis, language models, and latent semantic analysis to automatically associate colors to words. We compare these methods against a gold standard obtained via crowd-sourcing. The results show that each method is effective in capturing different aspects of word-color associations.


Target Word Latent Semantic Analysis Unsupervised Method Basic Color Associate Color 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gözde Özbal
    • 1
  • Carlo Strapparava
    • 1
  • Rada Mihalcea
    • 2
  • Daniele Pighin
    • 3
  1. 1.FBKTrentoItaly
  2. 2.UNTDentonUSA
  3. 3.UPCBarcelonaSpain

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