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학술지 A Prior Model of Structural SVMs for Domain Adaptation
Cited 12 time in scopus Download 0 time Share share facebook twitter linkedin kakaostory
저자
이창기, 장명길
발행일
201110
출처
ETRI Journal, v.33 no.5, pp.712-719
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.11.0110.0571
협약과제
10MS2400, 웹 QA 기술개발, 장명길
초록
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.
키워드
Domain adaptation, PRIOR model for structural SVMs., Structural SVMs
KSP 제안 키워드
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