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2016 | OriginalPaper | Buchkapitel

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

verfasst von : André Freitas, Siamak Barzegar, Juliano Efson Sales, Siegfried Handschuh, Brian Davis

Erschienen in: Knowledge Engineering and Knowledge Management

Verlag: Springer International Publishing

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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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Titel: Semantic Relatedness for All (Languages): A Comparative Analysis of Multilingual Semantic Relatedness Using Machine Translation
verfasst von: André Freitas
Siamak Barzegar
Juliano Efson Sales
Siegfried Handschuh
Brian Davis
Verlag: Springer International Publishing
Buch: Knowledge Engineering and Knowledge Management
Print ISBN: 978-3-319-49003-8

Electronic ISBN: 978-3-319-49004-5

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-49004-5_14

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