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Toward Effective Processing of Information Graphics in Multimodal Documents: A Bayesian Network Approach

  • Sandra Carberry
  • Stephanie Elzer
Part of the Studies in Computational Intelligence book series (SCI, volume 96)

Information graphics (non-pictorial graphics such as bar charts and line graphs) are an important component of multimodal documents. When information graphics appear in popular media, such as newspapers and magazines, they generally have a message that they are intended to convey. This chapter addresses the problem of understanding such information graphics. The chapter presents a corpus study that shows the importance of taking information graphics into account when processing a multimodal document. It then presents a Bayesian network approach to identifying the message conveyed by one kind of information graphic, simple bar charts, along with an evaluation of the graph understanding system. This work is the first (1) to demonstrate the necessity of understanding information graphics and taking their communicative goal into account when processing a multimodal document and (2) to develop a computational strategy for recognizing the communicative goal or intended message of an information graphic.

Keywords

Bayesian Network Perceptual Task Communicative Signal Information Graphic Conditional Probability Table 
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 2008

Authors and Affiliations

  • Sandra Carberry
    • 1
  • Stephanie Elzer
    • 2
  1. 1.Department of Computer ScienceUniversity of DelawareNewarkDE
  2. 2.Department of Computer ScienceMillersville UniversityPennsylvaniaPA

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