상세정보
학술지
A Prior Model of Structural SVMs for Domain Adaptation
- 발행일
- 201110
- 출처
- ETRI Journal, v.33 no.5, pp.712-719
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 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.
- 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