An experiment of word sense disambiguation in a machine translation system

In this paper, we demonstrate an experiment of a machine translation (MT) system for two different languages, English and Persian. We also describe a model for word sense disambiguation (WSD) task inside the MT system, which uses decision trees automatically learned from a training data set, as its disambiguation formalism. Our evaluations can be divided into two different categories: evaluation on the whole MT system and evaluation on the WSD component. The experiments on the whole MT, shows that this system gets 16% with respect to NIST measure, while the evaluation on WSD using a corpus contains 860 aligned sentences shows that this component disambiguates 81.4% of ambiguous word correctly.