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ACL
2007
15 years 3 months ago
Instance Weighting for Domain Adaptation in NLP
Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains. In this paper, we study the domain adaptation prob...
Jing Jiang, ChengXiang Zhai
CAISE
2005
Springer
15 years 7 months ago
A Method for Ontology Modeling in the Business Domain
Today ontology languages present a syntax which looks not “natural” and are lacking of built-in primitives (i.e., modeling notions) domain experts are familiar with. In this pa...
Michele Missikoff, Federica Schiappelli
EMNLP
2006
15 years 3 months ago
Domain Adaptation with Structural Correspondence Learning
Discriminative learning methods are widely used in natural language processing. These methods work best when their training and test data are drawn from the same distribution. For...
John Blitzer, Ryan T. McDonald, Fernando Pereira
FLAIRS
2001
15 years 3 months ago
Structural Learning in Object Oriented Domains
When constructing a Bayesian network, it can be advantageous to employ structural learning algorithms to combine knowledge captured in databases with prior information provided by...
Olav Bangsø, Helge Langseth, Thomas D. Niel...
ICML
2005
IEEE
16 years 2 months ago
Explanation-Augmented SVM: an approach to incorporating domain knowledge into SVM learning
We introduce a novel approach to incorporating domain knowledge into Support Vector Machines to improve their example efficiency. Domain knowledge is used in an Explanation Based ...
Qiang Sun, Gerald DeJong