Exploiting the Translation Context for Multilingual WSD

We propose a strategy to support Word Sense Disambiguation (WSD) which is designed specifically for multilingual applications, such as Machine Translation. Co-occurrence information extracted from the translation context, i.e., the set of words which have already been translated, is used to define the order in which disambiguation rules produced by a machine learning algorithm are applied. Experiments on the English-Portuguese translation of seven verbs yielded a significant improvement on the accuracy of a rule-based model: from 0.75 to 0.79.