Word translation disambiguation using bilingual bootstrapping

Abstract

This paper proposes a new method for word translation disambiguation using a machine learning technique called 'Bilingual Bootstrapping'. Bilingual Bootstrapping makes use of, in learning, a small number of classified data and a large number of unclassified data in the source and the target languages in translation. It constructs classifiers in the two languages in parallel and repeatedly boosts the performances of the classifiers by further classifying data in each of the two languages and by exchanging between the two languages information regarding the classified data. Experimental results indicate that word translation disambiguation based on Bilingual Bootstrapping consistently and significantly outperforms the existing methods based on 'Monolingual Bootstrapping'.