Open AccessProceedings Article
Frustratingly Easy Domain Adaptation
Hal Daumé
- 01 Jun 2007
- pp 256-263
1.7K
TL;DR: This work describes an approach to domain adaptation that is appropriate exactly in the case when one has enough “target” data to do slightly better than just using only “source’ data.
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Abstract: We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough “target” data to do slightly better than just using only “source” data. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!) and outperforms stateof-the-art approaches on a range of datasets. Moreover, it is trivially extended to a multidomain adaptation problem, where one has data from a variety of different domains.
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References
•Proceedings Article
Analysis of Representations for Domain Adaptation
Shai Ben-David,John Blitzer,Koby Crammer,Fernando Pereira +3 more
- 04 Dec 2006
TL;DR: The theory illustrates the tradeoffs inherent in designing a representation for domain adaptation and gives a new justification for a recently proposed model which explicitly minimizes the difference between the source and target domains, while at the same time maximizing the margin of the training set.
Domain Adaptation with Structural Correspondence Learning
John Blitzer,Ryan McDonald,Fernando Pereira +2 more
- 22 Jul 2006
TL;DR: This work introduces structural correspondence learning to automatically induce correspondences among features from different domains in order to adapt existing models from a resource-rich source domain to aresource-poor target domain.
Search-based structured prediction
TL;DR: Searn is an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision and comes with a strong, natural theoretical guarantee: good performance on the derived classification problems implies goodperformance on the structured prediction problem.
Domain adaptation for statistical classifiers
Hal Daumé,Daniel Marcu +1 more
TL;DR: This work introduces a statistical formulation of this problem in terms of a simple mixture model and presents an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts and leads to improved performance on three real world tasks on four different data sets from the natural language processing domain.