Paper: Exploiting Parallel Texts For Word Sense Disambiguation: An Empirical Study

ACL ID P03-1058
Title Exploiting Parallel Texts For Word Sense Disambiguation: An Empirical Study
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2003
Authors

A central problem of word sense disam- biguation (WSD) is the lack of manually sense-tagged data required for supervised learning. In this paper, we evaluate an ap- proach 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 diffi- cult SENSEVAL-2 nouns, the accuracy difference between the two approaches is only 14.0%, and the difference could nar- row 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 evalu- at...