Sciweavers

Share
ACL
2003

Exploiting Parallel Texts for Word Sense Disambiguation: An Empirical Study

10 years 2 months ago
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 sensetagged 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.
Hwee Tou Ng, Bin Wang, Yee Seng Chan
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where ACL
Authors Hwee Tou Ng, Bin Wang, Yee Seng Chan
Comments (0)
books