Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
The domain dependence of parsing
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A robust risk minimization based named entity recognition system
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Semi-supervised nonlinear dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Simple, robust, scalable semi-supervised learning via expectation regularization
Proceedings of the 24th international conference on Machine learning
Learning from measurements in exponential families
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Distributional representations for handling sparsity in supervised sequence-labeling
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Phrase clustering for discriminative learning
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Improving generative statistical parsing with semi-supervised word clustering
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
A theory of learning from different domains
Machine Learning
Improved extraction assessment through better language models
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Word representations: a simple and general method for semi-supervised learning
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Posterior Regularization for Structured Latent Variable Models
The Journal of Machine Learning Research
From frequency to meaning: vector space models of semantics
Journal of Artificial Intelligence Research
Exploring representation-learning approaches to domain adaptation
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Frustratingly easy semi-supervised domain adaptation
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
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Representation learning is a promising technique for discovering features that allow supervised classifiers to generalize from a source domain dataset to arbitrary new domains. We present a novel, formal statement of the representation learning task. We argue that because the task is computationally intractable in general, it is important for a representation learner to be able to incorporate expert knowledge during its search for helpful features. Leveraging the Posterior Regularization framework, we develop an architecture for incorporating biases into representation learning. We investigate three types of biases, and experiments on two domain adaptation tasks show that our biased learners identify significantly better sets of features than unbiased learners, resulting in a relative reduction in error of more than 16% for both tasks, with respect to existing state-of-the-art representation learning techniques.