Short-Text Similarity Measurement Using Word Sense Disambiguation and Synonym Expansion

  • Khaled Abdalgader
  • Andrew Skabar
Conference paper

DOI: 10.1007/978-3-642-17432-2_44

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6464)
Cite this paper as:
Abdalgader K., Skabar A. (2010) Short-Text Similarity Measurement Using Word Sense Disambiguation and Synonym Expansion. In: Li J. (eds) AI 2010: Advances in Artificial Intelligence. AI 2010. Lecture Notes in Computer Science, vol 6464. Springer, Berlin, Heidelberg

Abstract

Measuring the similarity between text fragments at the sentence level is made difficult by the fact that two sentences that are semantically related may not contain any words in common. This means that standard IR measures of text similarity, which are based on word co-occurrence and designed to operate at the document level, are not appropriate. While various sentence similarity measures have been recently proposed, these measures do not fully utilise the semantic information available from lexical resources such as WordNet. In this paper we propose a new sentence similarity measure which uses word sense disambiguation and synonym expansion to provide a richer semantic context to measure sentence similarity. Evaluation of the measure on three benchmark datasets shows that as a stand-alone sentence similarity measure, the method achieves better results than other methods recently reported in the literature.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Khaled Abdalgader
    • 1
  • Andrew Skabar
    • 1
  1. 1.Department of Computer Science and Computer EngineeringLa Trobe UniversityBundooraAustralia

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