Making large-scale support vector machine learning practical
Advances in kernel methods
Domain adaptation of information extraction models
ACM SIGMOD Record
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Domain adaptation via transfer component analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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Here we propose a novel approach for the task of domain adaptation for Natural Language Processing. Our approach captures relations between the source and target domains by applying a model transformation mechanism which can be learnt by using labeled data of limited size taken from the target domain. Experimental results on several Opinion Mining datasets show that our approach significantly outperforms baselines and published systems when the amount of labeled data is extremely small.