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EMNLP
2006

Domain Adaptation with Structural Correspondence Learning

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Domain Adaptation with Structural Correspondence Learning
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 resourcerich 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.
John Blitzer, Ryan T. McDonald, Fernando Pereira
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where EMNLP
Authors John Blitzer, Ryan T. McDonald, Fernando Pereira
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