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» Boosting for Text Classification with Semantic Features
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AUSDM
2006
Springer
144views Data Mining» more  AUSDM 2006»
13 years 9 months ago
A Characterization of Wordnet Features in Boolean Models For Text Classification
Supervised text classification is the task of automatically assigning a category label to a previously unlabeled text document. We start with a collection of pre-labeled examples ...
Trevor N. Mansuy, Robert J. Hilderman
FLAIRS
2006
13 years 6 months ago
Evaluating WordNet Features in Text Classification Models
Incorporating semantic features from the WordNet lexical database is among one of the many approaches that have been tried to improve the predictive performance of text classifica...
Trevor N. Mansuy, Robert J. Hilderman
EMNLP
2006
13 years 6 months ago
Boosting Unsupervised Relation Extraction by Using NER
Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional IE methods, the ...
Ronen Feldman, Benjamin Rosenfeld
CICLING
2009
Springer
14 years 5 months ago
Semantic Clustering for a Functional Text Classification Task
Abstract. We describe a semantic clustering method designed to address shortcomings in the common bag-of-words document representation for functional semantic classification tasks....
Thomas Lippincott, Rebecca J. Passonneau
SDM
2008
SIAM
133views Data Mining» more  SDM 2008»
13 years 6 months ago
Semantic Smoothing for Bayesian Text Classification with Small Training Data
Bayesian text classifiers face a common issue which is referred to as data sparsity problem, especially when the size of training data is very small. The frequently used Laplacian...
Xiaohua Zhou, Xiaodan Zhang, Xiaohua Hu