Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning associative Markov networks
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Information extraction from research papers using conditional random fields
Information Processing and Management: an International Journal
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Efficient inference with cardinality-based clique potentials
Proceedings of the 24th international conference on Machine learning
Foundations and Trends in Databases
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Opinion mining by transformation-based domain adaptation
TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
Robust web data extraction: a novel approach based on minimum cost script edit model
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
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Domain adaptation refers to the process of adapting an extraction model trained in one domain to another related domain with only unlabeled data. We present a brief survey of existing methods of retraining models to best exploit labeled data from a related domain. These approaches that involve expensive model retraining are not practical when a large number of new domains have to be handled in an operational setting. We describe our approach for adapting record extraction models that exploits the regularity within a domain to jointly label records without retraining any model.