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» Reducing the human overhead in text categorization
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KDD
2006
ACM
118views Data Mining» more  KDD 2006»
14 years 5 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
DGO
2008
113views Education» more  DGO 2008»
13 years 6 months ago
A study in rule-specific issue categorization for e-rulemaking
We address the e-rulemaking problem of categorizing public comments according to the issues that they address. In contrast to previous text categorization research in e-rulemaking...
Claire Cardie, Cynthia Farina, Adil Aijaz, Matt Ra...
WWW
2006
ACM
14 years 5 months ago
Large-scale text categorization by batch mode active learning
Large-scale text categorization is an important research topic for Web data mining. One of the challenges in large-scale text categorization is how to reduce the amount of human e...
Steven C. H. Hoi, Rong Jin, Michael R. Lyu
ICAC
2005
IEEE
13 years 10 months ago
PICCIL: Interactive Learning to Support Log File Categorization
Motivated by the real-world application of categorizing system log messages into defined situation categories, this paper describes an interactive text categorization method, PICC...
David Loewenstern, Sheng Ma, Abdi Salahshour
DGO
2008
126views Education» more  DGO 2008»
13 years 6 months ago
Active learning for e-rulemaking: public comment categorization
We address the e-rulemaking problem of reducing the manual labor required to analyze public comment sets. In current and previous work, for example, text categorization techniques...
Stephen Purpura, Claire Cardie, Jesse Simons