In this paper we explore robustness and domain adaptation issues for Word Sense Disambiguation (WSD) using Singular Value Decomposition (SVD) and unlabeled data. We focus on the semi-supervised domain adaptation scenario, where we train on the source corpus and test on the target corpus, and try to improve results using unlabeled data. Our method yields up to 16.3% error reduction compared to state-of-the-art systems, being the first to report successful semi-supervised domain adaptation. Surprisingly the improvement comes from the use of unlabeled data from the source corpus, and not from the target corpora, meaning that we get robustness rather than domain adaptation. In addition, we study the behavior of our system on the target domain.
REF worked on the semi-supervised DA of WSD.
On Robustness and Domain Adaptation using SVD for Word Sense Disambiguation
International Conference On Computational Linguistics
Computer Science