Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Feature Selection for Unbalanced Class Distribution and Naive Bayes
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Nightmare at test time: robust learning by feature deletion
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Semi-supervised conditional random fields for improved sequence segmentation and labeling
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Exploiting domain structure for named entity recognition
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
EfficientL1regularized logistic regression
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Actively Transfer Domain Knowledge
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Vertical selection in the presence of unlabeled verticals
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Domain adaptation by constraining inter-domain variability of latent feature representation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Multi-task clustering via domain adaptation
Pattern Recognition
Semi-supervised multi-task learning of structured prediction models for web information extraction
Proceedings of the 20th ACM international conference on Information and knowledge management
Attribute and object selection queries on objects with probabilistic attributes
ACM Transactions on Database Systems (TODS)
On minimum distribution discrepancy support vector machine for domain adaptation
Pattern Recognition
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The goal in domain adaptation is to train a model using labeled data sampled from a domain different from the target domain on which the model will be deployed. We exploit unlabeled data from the target domain to train a model that maximizes likelihood over the training sample while minimizing the distance between the training and target distribution. Our focus is conditional probability models used for predicting a label structure ygiven input xbased on features defined jointly over xand y. We propose practical measures of divergence between the two domains based on which we penalize features with large divergence, while improving the effectiveness of other less deviant correlated features. Empirical evaluation on several real-life information extraction tasks using Conditional Random Fields (CRFs) show that our method of domain adaptation leads to significant reduction in error.