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» Learning Classifiers from Semantically Heterogeneous Data
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EMNLP
2006
13 years 10 months ago
Semantic Role Labeling via Instance-Based Learning
This paper demonstrates two methods to improve the performance of instancebased learning (IBL) algorithms for the problem of Semantic Role Labeling (SRL). Two IBL algorithms are u...
Chi-san Althon Lin, Tony C. Smith
IJCV
2008
106views more  IJCV 2008»
13 years 9 months ago
Evaluation of Localized Semantics: Data, Methodology, and Experiments
We present a new data set encoding localized semantics for 1014 images and a methodology for using this kind of data for recognition evaluation. This methodology establishes protoc...
Kobus Barnard, Quanfu Fan, Ranjini Swaminathan, An...
ICML
2005
IEEE
14 years 9 months ago
Recycling data for multi-agent learning
Learning agents can improve performance cooperating with other agents, particularly learning agents forming a committee outperform individual agents. This "ensemble effect&qu...
Santiago Ontañón, Enric Plaza
ICML
2006
IEEE
14 years 9 months ago
Nightmare at test time: robust learning by feature deletion
When constructing a classifier from labeled data, it is important not to assign too much weight to any single input feature, in order to increase the robustness of the classifier....
Amir Globerson, Sam T. Roweis
ACL
2003
13 years 10 months ago
Clustering Polysemic Subcategorization Frame Distributions Semantically
Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated corpus data. We describe a new approach which involves clusteri...
Anna Korhonen, Yuval Krymolowski, Zvika Marx