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Random Indexing Revisited

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Natural Language Processing and Information Systems (NLDB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9103))

Abstract

Random indexing is a method for constructing vector spaces at a reduced dimensionality. Previously, the method has been proposed using Kanerva’s sparse distributed memory model. Although intuitively plausible, this description fails to provide mathematical justification for setting the method’s parameters. The random indexing method is revisited using the principles of sparse random projections in Euclidean spaces in order to complement its previous delineation.

B. QasemiZadeh—This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant Number SFI/12/RC/2289.

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Notes

  1. 1.

    An extension to this discussion and some empirical experiments can be seen in [15].

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Authors and Affiliations

  1. National University of Ireland, Galway and University of Passau, Passau, Germany

    Behrang QasemiZadeh

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  1. Behrang QasemiZadeh
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Correspondence to Behrang QasemiZadeh .

Editor information

Editors and Affiliations

  1. Technische Universität Darmstadt, Darmstadt, Germany

    Chris Biemann

  2. Universität Passau, Passau, Germany

    Siegfried Handschuh

  3. Universität Passau, Passau, Germany

    André Freitas

  4. University of Salford, Salford, United Kingdom

    Farid Meziane

  5. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

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QasemiZadeh, B. (2015). Random Indexing Revisited. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_43

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  • DOI: https://doi.org/10.1007/978-3-319-19581-0_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19580-3

  • Online ISBN: 978-3-319-19581-0

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