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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.
The service is available athttp://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
Department of Computer Science and Mathematics, University of Passau, Innstrasse 43, ITZ-110, 94032, Passau, Germany
André Freitas, Juliano Efson Sales & Siegfried Handschuh
Insight Centre for Data Analytics, National University of Ireland, Galway, IDA Business Park, Lower Dangan, Galway, Ireland
Siamak Barzegar & Brian Davis
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Correspondence toAndré Freitas.
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Linköping University, Linköping, Sweden
Eva Blomqvist
University of Bologna, Bologna, Italy
Paolo Ciancarini
University of Bologna, Bologna, Italy
Francesco Poggi
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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