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

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Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10024))

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Abstract

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).

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Notes

  1. 1.

    The service is available at http://rebrand.ly/dinfra.

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Acknowledgments

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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Authors and Affiliations

  1. Department of Computer Science and Mathematics, University of Passau, Innstrasse 43, ITZ-110, 94032, Passau, Germany

    André Freitas, Juliano Efson Sales & Siegfried Handschuh

  2. Insight Centre for Data Analytics, National University of Ireland, Galway, IDA Business Park, Lower Dangan, Galway, Ireland

    Siamak Barzegar & Brian Davis

Authors
  1. André Freitas
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  2. Siamak Barzegar
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  3. Juliano Efson Sales
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  4. Siegfried Handschuh
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  5. Brian Davis
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Corresponding author

Correspondence to André Freitas .

Editor information

Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Eva Blomqvist

  2. University of Bologna, Bologna, Italy

    Paolo Ciancarini

  3. University of Bologna, Bologna, Italy

    Francesco Poggi

  4. University of Bologna, Bologna, Italy

    Fabio Vitali

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Freitas, A., Barzegar, S., Sales, J.E., Handschuh, S., Davis, B. (2016). Semantic Relatedness for All (Languages): A Comparative Analysis of Multilingual Semantic Relatedness Using Machine Translation. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_14

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  • DOI: https://doi.org/10.1007/978-3-319-49004-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49003-8

  • Online ISBN: 978-3-319-49004-5

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