Distributional semantics from text and images

  • Authors:
  • Elia Bruni;Giang Binh Tran;Marco Baroni

  • Affiliations:
  • CIMeC, University of Trento;EMLCT, Free University of Bolzano & CIMeC, University of Trento;CIMeC, University of Trento

  • Venue:
  • GEMS '11 Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics
  • Year:
  • 2011

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Abstract

We present a distributional semantic model combining text- and image-based features. We evaluate this multimodal semantic model on simulating similarity judgments, concept clustering and the BLESS benchmark. When integrated with the same core text-based model, image-based features are at least as good as further text-based features, and they capture different qualitative aspects of the tasks, suggesting that the two sources of information are complementary.