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Measuring Semantic Similarity Between Words Using Lexical Knowledge and Neural Networks

  • Yuhua Li
  • Zuhair Bandar
  • David Mclean
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)

Abstract

This paper investigates the determination of semantic similarity by the incorporation of structural semantic knowledge from a lexical database and the learning ability of neural networks. The lexical database is assumed to be organised in a hierarchical structure. The extracted lexical knowledge contains the relative location of the concerned words in the lexical hierarchy. The neural network then processes available lexical knowledge to provide semantic similarity for words. Experimental evaluation against a benchmark set of human similarity ratings demonstrates that the proposed method is effective in measuring semantic similarity between words.

Keywords

Semantic Similarity Word Pair Short Path Length Human Rating Lexical Database 
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 2002

Authors and Affiliations

  • Yuhua Li
    • 1
  • Zuhair Bandar
    • 1
  • David Mclean
    • 1
  1. 1.Intelligent System Group, Department of Computing and MathematicsManchester Metropolitan UniversityManchesterEngland

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