Paper: Supervised Domain Adaption for WSD

ACL ID E09-1006
Title Supervised Domain Adaption for WSD
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2009

The lack of positive results on super- vised domain adaptation for WSD have cast some doubts on the utility of hand- tagging general corpora and thus devel- oping generic supervised WSD systems. In this paper we show for the first time that our WSD system trained on a general sourcecorpus(BNC)andthetargetcorpus, obtains up to 22% error reduction when compared to a system trained on the tar- get corpus alone. In addition, we show that as little as 40% of the target corpus (when supplemented with the source cor- pus) is sufficient to obtain the same results as training on the full target data. The key for success is the use of unlabeled data with SVD, a combination of kernels and SVM.