Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
Bridged Refinement for Transfer Learning
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Intra-document structural frequency features for semi-supervised domain adaptation
Proceedings of the 17th ACM conference on Information and knowledge management
Domain adaptation from multiple sources via auxiliary classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Co-adaptation: Adaptive co-training for semi-supervised learning
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Multi-domain learning by confidence-weighted parameter combination
Machine Learning
The rademacher complexity of linear transformation classes
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Domain adaptation to summarize human conversations
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Language models as representations for weakly-supervised NLP tasks
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Domain adaptation techniques for machine translation and their evaluation in a real-world setting
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Multisource domain adaptation and its application to early detection of fatigue
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
Nudging the envelope of direct transfer methods for multilingual named entity recognition
WILS '12 Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure
Linking named entities to any database
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Multi-domain learning: when do domains matter?
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Biased representation learning for domain adaptation
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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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.