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Journal Article A Prior Model of Structural SVMs for Domain Adaptation
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Authors
Changki Lee, Myung-Gil Jang
Issue Date
2011-10
Citation
ETRI Journal, v.33, no.5, pp.712-719
ISSN
1225-6463
Publisher
한국전자통신연구원 (ETRI)
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.4218/etrij.11.0110.0571
Project Code
10MS2400, Development of Web QA(Question Answering) Technology, Jang Myung Gil
Abstract
In this paper, we study the problem of domain adaptation for structural support vector machines (SVMs). We consider a number of domain adaptation approaches for structural SVMs and evaluate them on named entity recognition, part-of-speech tagging, and sentiment classification problems. Finally, we show that a prior model for structural SVMs outperforms other domain adaptation approaches in most cases. Moreover, the training time for this prior model is reduced compared to other domain adaptation methods with improvements in performance. © 2011 Optical Society of America.
KSP Keywords
Classification problems, Named Entity Recognition, Part of Speech(POS), Part-Of-Speech Tagging, Sentiment classification, Structural SVM(SSVM), Support VectorMachine(SVM), Training time, domain adaptation, prior model