A Geometric Algebra Based Distributional Model to Encode Sentences Semantics

  • Agnese Augello
  • Manuel Gentile
  • Giovanni Pilato
  • Giorgio Vassallo
Part of the Studies in Computational Intelligence book series (SCI, volume 515)


Word space models are used to encode the semantics of natural language elements by means of high dimensional vectors [23]. Latent Semantic Analysis (LSA) methodology [15] is well known and widely used for its generalization properties. Despite of its good performance in several applications, the model induced by LSA ignores dynamic changes in sentences meaning that depend on the order of the words, because it is based on a bag of words analysis. In this chapter we present a technique that exploits LSA-based semantic spaces and geometric algebra in order to obtain a sub-symbolic encoding of sentences taking into account the words sequence in the sentence.


Semantic spaces Sentences encoding Clifford algebra 



We are grateful to Professor Thomas Landauer, to Praful Mangalath and the Institute of Cognitive Science of the University of Colorado Boulder for providing us the TASA corpus. This work has been partially supported by the PON01_01687—SINTESYS (Security and INTElligence SYSstem) Research Project.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Agnese Augello
    • 1
  • Manuel Gentile
    • 2
  • Giovanni Pilato
    • 1
  • Giorgio Vassallo
    • 3
  1. 1.ICARPalermoItaly
  2. 2.ITDPalermoItaly
  3. 3.DICGIM Università di PalermoPalermoItaly

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