Expanding wordnets to new languages with multilingual sense disambiguation
Date
2016-12-11Author
Arcan, Mihael
McCrae, John P.
Buitelaar, Paul
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Arcan, Mihael, McCrae, John P., & Buitelaar, Paul. (2016). Expanding wordnets to new languages with multilingual sense disambiguation. Paper presented at the COLING 2016, the 26th International Conference on Computational Linguistics, Osaka, Japan, 11-16 December.
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Abstract
Princeton WordNet is one of the most important resources for natural language processing, but
is only available for English. While it has been translated using the expand approach to many
other languages, this is an expensive manual process. Therefore it would be beneficial to have a
high-quality automatic translation approach that would support NLP techniques, which rely on
WordNet in new languages. The translation of wordnets is fundamentally complex because of the
need to translate all senses of a word including low frequency senses, which is very challenging
for current machine translation approaches. For this reason we leverage existing translations
of WordNet in other languages to identify contextual information for wordnet senses from a
large set of generic parallel corpora. We evaluate our approach using 10 translated wordnets for
European languages. Our experiment shows a significant improvement over translation without
any contextual information. Furthermore, we evaluate how the choice of pivot languages affects
performance of multilingual word sense disambiguation.