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A Weighted Boolean Model for Cross-Language Text Retrieval

  • David Hull
Chapter
Part of the The Springer International Series on Information Retrieval book series (INRE, volume 2)

Abstract

Dictionary-based cross-language text retrieval systems must find a way to deal with the ambiguity associated with language translation. In this chapter, we claim that the use of conjunction in boolean models leads to simple, automatic disambiguation in the target language. We derive a new weighted boolean model based on probabilistic principles and test it on the cross-language text retrieval problem. The results suggest that while the weighted boolean model is highly effective in general retrieval situations, more experimental evidence needs to be gathered before we can state conclusively that it is particularly advantageous for cross-language applications. However, preliminary evidence suggests that the model is quite promising.

Keywords

Machine Translation Vector Model Query Term Query Expansion Vector Space Model 
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 Science+Business Media New York 1998

Authors and Affiliations

  • David Hull
    • 1
  1. 1.Xerox Research Centre EuropeGrenoble LaboratoryMeylanFrance

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