Semantic Relatedness for All (Languages): A Comparative Analysis of Multilingual Semantic Relatedness Using Machine Translation

  • André Freitas
  • Siamak Barzegar
  • Juliano Efson Sales
  • Siegfried Handschuh
  • Brian Davis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10024)


This paper provides a comparative analysis of the performance of four state-of-the-art distributional semantic models (DSMs) over 11 languages, contrasting the native language-specific models with the use of machine translation over English-based DSMs. The experimental results show that there is a significant improvement (average of 16.7 % for the Spearman correlation) by using state-of-the-art machine translation approaches. The results also show that the benefit of using the most informative corpus outweighs the possible errors introduced by the machine translation. For all languages, the combination of machine translation over the Word2Vec English distributional model provided the best results consistently (average Spearman correlation of0.68).


Multilingual distributional semantics Machine translation 



This publication has emanated from research supported by the National Council for Scientific and Technological Development, Brazil (CNPq) and by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • André Freitas
    • 1
  • Siamak Barzegar
    • 2
  • Juliano Efson Sales
    • 1
  • Siegfried Handschuh
    • 1
  • Brian Davis
    • 2
  1. 1.Department of Computer Science and MathematicsUniversity of PassauPassauGermany
  2. 2.Insight Centre for Data AnalyticsNational University of Ireland, GalwayGalwayIreland

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