2006 DomainAdaptationwithStructuralC

• (Blitzer et al., 2006) ⇒ John Blitzer, Ryan McDonald, and Fernando Pereira. (2006). “Domain Adaptation with Structural Correspondence Learning.” In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. ISBN:1-932432-73-6

Subject Headings: Transfer Learning Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222006%22+Domain+Adaptation+with+Structural+Correspondence+Learning
• http://dl.acm.org/citation.cfm?id=1610075.1610094&preflayout=flat#citedby

Quotes

Abstract

Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For many NLP tasks, however, we are confronted with new domains in which labeled data is scarce or non-existent. In such cases, we seek to adapt existing models from a resource-rich source domain to a resource-poor target domain. We introduce structural correspondence learning to automatically induce correspondences among features from different domains. We test our technique on part of speech tagging and show performance gains for varying amounts of source and target training data, as well as improvements in target domain parsing accuracy using our improved tagger.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 DomainAdaptationwithStructuralC	Ryan T. McDonald Fernando Pereira John Blitzer			Domain Adaptation with Structural Correspondence Learning						2006