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Probabilistic Topic Maps: Navigating through Large Text Collections

  • Thomas Hofmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1642)

Abstract

The visualization of large text databases and document collections is an important step towards more flexible and interactive types of information retrieval. This paper presents a probabilistic approach which combines a statistical, model—based analysis with a topological visualization principle. Our method can be utilized to derive topic maps which represent topical information by characteristic keyword distributions arranged in a two—dimensional spatial layout. Combined with multi-resolution techniques this provides a three-dimensional space for interactive information navigation in large text collections.

Keywords

Document Collection Latent Semantic Analysis Latent Class Model Probabilistic Latent Semantic Analysis Dimensional Grid 
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 1999

Authors and Affiliations

  • Thomas Hofmann
    • 1
  1. 1.Computer Science DivisionUC Berkeley & International CS InstituteBerkeley

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