A study on similarity and relatedness using distributional and WordNet-based approaches

  • Authors:
  • Eneko Agirre;Enrique Alfonseca;Keith Hall;Jana Kravalova;Marius Paşca;Aitor Soroa

  • Affiliations:
  • University of the Basque Country;Google Inc.;Google Inc.;Google Inc. and Charles University in Prague;Google Inc.;University of the Basque Country

  • Venue:
  • NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
  • Year:
  • 2009

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Abstract

This paper presents and compares WordNet-based and distributional similarity approaches. The strengths and weaknesses of each approach regarding similarity and relatedness tasks are discussed, and a combination is presented. Each of our methods independently provide the best results in their class on the RG and WordSim353 datasets, and a supervised combination of them yields the best published results on all datasets. Finally, we pioneer cross-lingual similarity, showing that our methods are easily adapted for a cross-lingual task with minor losses.