Exploiting Parallel Texts for Word Sense Disambiguation: An Empirical Study 
 
A central problem of word sense disambiguation (WSD) is the lack of manually sense-tagged data required for supervised learning . In this paper, we evaluate an approach to automatically acquire sense-tagged training data from English-Chinese parallel corpora , which are then used for disambiguating the nouns in the SENSEVAL-2 English lexical sample task . Our investigation reveals that this method of acquiring sense-tagged data is promising. On a subset of the most difficult SENSEVAL-2 nouns , the accuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage . Our analysis also highlights the importance of the issue of domain dependence in evaluating WSD programs . 