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COLING
2008

On Robustness and Domain Adaptation using SVD for Word Sense Disambiguation

10 years 3 months ago
On Robustness and Domain Adaptation using SVD for Word Sense Disambiguation
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.
Eneko Agirre, Oier Lopez de Lacalle
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where COLING
Authors Eneko Agirre, Oier Lopez de Lacalle
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