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Fast Extraction of Semantic Features from a Latent Semantic Indexed Text Corpus

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Abstract

This paper proposes a projection-based symmetrical factorisation method for extracting semantic features from collections of text documents stored in a Latent Semantic space. Preliminary experimental results demonstrate this yields a comparable representation to that provided by a novel probabilistic approach which reconsiders the entire indexing problem of text documents and works directly in the original high dimensional vector-space representation of text. The employed projection index is derived here from the a priori constraints on the problem. The principal advantage of this approach is computational efficiency and is obtained by the exploitation of the Latent Semantic Indexing as a preprocessing stage. Simulation results on subsets of the 20-Newsgroups text corpus in various settings are provided.

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Authors and Affiliations

  1. Laboratory of Computer and Information Science, Helsinki University of Technology, P.O. Box 5400, FIN-02015, HUT, Finland

    A. Kabán & M. A. Girolami

Authors
  1. A. Kabán
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  2. M. A. Girolami
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Kabán, A., Girolami, M.A. Fast Extraction of Semantic Features from a Latent Semantic Indexed Text Corpus. Neural Processing Letters 15, 31–43 (2002). https://doi.org/10.1023/A:1013801028884

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  • DOI: https://doi.org/10.1023/A:1013801028884

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