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Computationally Efficient Approximation of a Probabilistic Model for Document Representation in the WEBSOM Full-Text Analysis Method

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Abstract

WEBSOM is a recently developed neural method for exploring full-text document collections, for information retrieval, and for information filtering. In WEBSOM the full-text documents are encoded as vectors in a document space somewhat like in earlier information retrieval methods, but in WEBSOM the document space is formed in an unsupervised manner using the Self-Organizing Map algorithm. In this article the document representations the WEBSOM creates are shown to be computationally efficient approximations of the results of a certain probabilistic model. The probabilistic model incorporates information about the similarity of use of different words to take into account their semantic relations.

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Authors and Affiliations

  1. Helsinki University of Technology, Neural Networks Research Centre, Rakentajanaukio 2 C, FIN-02150, Espoo, Finland

    S. Kaski

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  1. S. Kaski
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Kaski, S. Computationally Efficient Approximation of a Probabilistic Model for Document Representation in the WEBSOM Full-Text Analysis Method. Neural Processing Letters 5, 69–81 (1997). https://doi.org/10.1023/A:1009618125967

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  • DOI: https://doi.org/10.1023/A:1009618125967

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