Filaments of Meaning in Word Space

  • Jussi Karlgren
  • Anders Holst
  • Magnus Sahlgren
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4956)


Word space models, in the sense of vector space models built on distributional data taken from texts, are used to model semantic relations between words. We argue that the high dimensionality of typical vector space models lead to unintuitive effects on modeling likeness of meaning and that the local structure of word spaces is where interesting semantic relations reside. We show that the local structure of word spaces has substantially different dimensionality and character than the global space and that this structure shows potential to be exploited for further semantic analysis using methods for local analysis of vector space structure rather than globally scoped methods typically in use today such as singular value decomposition or principal component analysis.


Latent Semantic Analysis Vector Space Model Retrieval Practice Left Tail Random Indexing 
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 2008

Authors and Affiliations

  • Jussi Karlgren
    • 1
  • Anders Holst
    • 1
  • Magnus Sahlgren
    • 1
  1. 1.Swedish Institute of Computer Science 

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