In this work, we propose a semisupervised extension to a well-known supervised domain adaptation approach (EA) (Daumé III, 2007) . Our proposed approach (EA++) builds on the notion of augmented space (introduced in EA) and harnesses unlabeled data in target domain to ameliorate the transfer of information from source to target. This semisupervised approach to domain adaptation is extremely simple to implement, and can be applied as a pre-processing step to any supervised learner. Experimental results on sequential labeling tasks demonstrate the efficacy of the proposed method.
REF then proposed a semi-supervised extension of the Easy Adaptation, which harnesses unlabeled target domain data to ameliorate the transfer of information from source to target.
Frustratingly Easy Semi-Supervised Domain Adaptation
Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Computer Science