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A Multisource Context-Dependent Semantic Distance Between Concepts

  • Ahmad El Sayed
  • Hakim Hacid
  • Djamel Zighed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4653)

Abstract

A major lack in the existing semantic similarity methods is that no one takes into account the context or the considered domain. However, two concepts similar in one context may appear completely unrelated in another context. In this paper, our first-level approach is context-dependent. We present a new method that computes semantic similarity in taxonomies by considering the context pattern of the text corpus. In addition, since taxonomies and corpora are interesting resources and each one has its strengths and weaknesses, we propose to combine similarity methods in our second-level multi-source approach. The performed experiments showed that our approach outperforms all the existing approaches.

Keywords

Semantic Similarity Word Pair Latent Semantic Analysis Semantic Distance Text Corpus 
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 2007

Authors and Affiliations

  • Ahmad El Sayed
    • 1
  • Hakim Hacid
    • 1
  • Djamel Zighed
    • 1
  1. 1.University of Lyon 2, ERIC Laboratory- 5, avenue Pierre Mendès-France, 69676 Bron cedexFrance

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