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Using the Structure of a Conceptual Network in Computing Semantic Relatedness

  • Iryna Gurevych
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3651)

Abstract

We present a new method for computing semantic relatedness of concepts. The method relies solely on the structure of a conceptual network and eliminates the need for performing additional corpus analysis. The network structure is employed to generate artificial conceptual glosses. They replace textual definitions proper written by humans and are processed by a dictionary based metric of semantic relatedness [1]. We implemented the metric on the basis of GermaNet, the German counterpart of WordNet, and evaluated the results on a German dataset of 57 word pairs rated by human subjects for their semantic relatedness. Our approach can be easily applied to compute semantic relatedness based on alternative conceptual networks, e.g. in the domain of life sciences.

Keywords

Semantic Similarity Word Pair Semantic Relatedness Human Judgment Word Sense 
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 2005

Authors and Affiliations

  • Iryna Gurevych
    • 1
  1. 1.EML Research gGmbHHeidelbergGermany

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