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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.

Domain Adaptation of Conditional Probability Models Via Feature Subsetting