Open AccessA Prior Model of Structural SVMs for Domain Adaptation
Author(s) -
Lee Changki,
Jang MyungGil
Publication year - 2011
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.11.0110.0571
Subject(s) - domain adaptation , support vector machine , computer science , adaptation (eye) , domain (mathematical analysis) , artificial intelligence , machine learning , pattern recognition (psychology) , mathematics , classifier (uml) , mathematical analysis , physics , optics
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.