Els Lefever* and Veronique Hoste**

LT3, University College Ghent, Groot–Brittannielaan 45, Ghent, Belgium and Dpt. of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281(S9), Ghent, Belgium (e–mail: *Els.Lefever@hogent.be, **Veronique.Hoste@hogent.be).

Manuscript received November 6, 2010.
Manuscript accepted for publication January 12, 2011.

This paper describes a set of experiments in which the viability of a classification–based Word Sense Disambiguation system that uses evidence from multiple languages is investigated. Instead of using a predefined monolingual sense–inventory such as WordNet, we use a language–independent framework and start from a manually constructed gold standard in which the word senses are made up by the translations that result from word alignments on a parallel corpus. To train and test the classifier, we used English as an input language and we incorporated the translations of our target words in five languages (viz. Spanish, Italian, French, Dutch and German) as features in the feature vectors. Our results show that the multilingual approach outperforms the classification experiments where no additional evidence from other languages is used. These results confirm our initial hypothesis that each language adds evidence to further refine the senses of a given word. This allows us to develop a proof of concept for a multilingual approach to Word Sense Disambiguation.

Key words: Word Sense Disambiguation, multilingual, cross–lingual.

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