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ACL
2008
13 years 7 months ago
Efficient, Feature-based, Conditional Random Field Parsing
Discriminative feature-based methods are widely used in natural language processing, but sentence parsing is still dominated by generative methods. While prior feature-based dynam...
Jenny Rose Finkel, Alex Kleeman, Christopher D. Ma...
JMLR
2008
230views more  JMLR 2008»
13 years 5 months ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
PKDD
2007
Springer
91views Data Mining» more  PKDD 2007»
13 years 12 months ago
Domain Adaptation of Conditional Probability Models Via Feature Subsetting
The goal in domain adaptation is to train a model using labeled data sampled from a domain different from the target domain on which the model will be deployed. We exploit unlabel...
Sandeepkumar Satpal, Sunita Sarawagi
CVPR
2010
IEEE
13 years 11 months ago
Semantic Context Modeling with Maximal Margin Conditional Random Fields for Automatic Image Annotation
Context modeling for Vision Recognition and Automatic Image Annotation (AIA) has attracted increasing attentions in recent years. For various contextual information and resources,...
Yu Xiang, Xiangdong Zhou, Zuotao Liu, Tat-seng chu...
IJCNLP
2005
Springer
13 years 11 months ago
Tense Tagging for Verbs in Cross-Lingual Context: A Case Study
The current work applies Conditional Random Fields to the problem of temporal reference mapping from Chinese text to English text. The learning algorithm utilizes a moderate number...
Yang Ye, Zhu Zhang