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Frustratingly Easy Domain Adaptation
TL;DR: In this paper, the authors 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.
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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 state-of-the-art approaches on a range of datasets. Moreover, it is trivially extended to a multi-domain adaptation problem, where one has data from a variety of different domains.
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Citations
•Proceedings Article
Automatic Domain Adaptation for Word Sense Disambiguation Based on Comparison of Multiple Classifiers
Kanako Komiya,Manabu Okumura +1 more
- 01 Nov 2012
TL;DR: In this paper, the authors compared three classifiers for three DA methods, where a classifier was trained with a small amount of target data that was randomly selected and manually labeled but without source data.
12
Dependency parsing with finite state transducers and compression rules
Pablo Gamallo,Marcos Garcia +1 more
TL;DR: The results show that the performance of the cross-lingual method does not change across related languages and across different treebanks, while most supervised methods turn out to be very dependent on the text domain used to train the system.
12
•Proceedings Article
Predicate-Argument Structure Analysis with Zero-Anaphora Resolution for Dialogue Systems
Kenji Imamura,Ryuichiro Higashinaka,Tomoko Izumi +2 more
- 01 Aug 2014
TL;DR: By incorporating parameter adaptation and automatically acquiring knowledge from large text corpora, this paper achieves a PASA specialized to dialogues that has higher accuracy than that for newspaper articles.
12
•Posted Content
Strategies for Conceptual Change in Convolutional Neural Networks.
TL;DR: It is shown, among other things, that across handwritten digits, natural images, and classical music, adaptive strategies are systematically more effective than a baseline method that starts learning from scratch.
12
The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance
Jeffrey P. Ferraro,Ye Ye,Per H. Gesteland,Peter J. Haug,Peter J. Haug,Fuchiang Rich Tsui,Gregory F. Cooper,Rudy E. Van Bree,Thomas Ginter,Andrew J. Nowalk,Michael M. Wagner +10 more
TL;DR: In all but one instance (influenza versus NI-ILI using IH cases), local Parsers were more effective at supporting case-detection although performances of non-local parsers were reasonable.
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.
•Posted Content
Search-based Structured Prediction
TL;DR: Searn as mentioned in this paper is a meta-algorithm that transforms complex structured prediction problems into simple classification problems to which any binary classifier may be applied, and it is able to learn prediction functions for any loss function and any class of features.
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•Proceedings Article
Adaptation of Maximum Entropy Capitalizer: Little Data Can Help a Lo.
Ciprian Chelba,Alex Acero +1 more
- 01 Jul 2004
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.