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» A Text Understander that Learns
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KDD
2006
ACM
118views Data Mining» more  KDD 2006»
16 years 6 months ago
Reducing the human overhead in text categorization
Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from...
Arnd Christian König, Eric Brill
MICAI
2007
Springer
16 years 16 days ago
Taking Advantage of the Web for Text Classification with Imbalanced Classes
A problem of supervised approaches for text classification is that they commonly require high-quality training data to construct an accurate classifier. Unfortunately, in many real...
Rafael Guzmán-Cabrera, Manuel Montes-y-G&oa...
CIKM
2008
Springer
15 years 8 months ago
Error-driven generalist+experts (edge): a multi-stage ensemble framework for text categorization
We introduce a multi-stage ensemble framework, ErrorDriven Generalist+Expert or Edge, for improved classification on large-scale text categorization problems. Edge first trains a ...
Jian Huang 0002, Omid Madani, C. Lee Giles
ICML
2003
IEEE
16 years 7 months ago
Text Bundling: Statistics Based Data-Reduction
As text corpora become larger, tradeoffs between speed and accuracy become critical: slow but accurate methods may not complete in a practical amount of time. In order to make the...
Lawrence Shih, Jason D. Rennie, Yu-Han Chang, Davi...
SYNASC
2007
IEEE
136views Algorithms» more  SYNASC 2007»
16 years 21 days ago
Wikipedia-Based Kernels for Text Categorization
In recent years several models have been proposed for text categorization. Within this, one of the widely applied models is the vector space model (VSM), where independence betwee...
Zsolt Minier, Zalan Bodo, Lehel Csató