A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Linear Least Squares Fit mapping method for information retrieval from natural language texts
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Classifying web documents in a hierarchy of categories: a comprehensive study
Journal of Intelligent Information Systems
Chinese text categorization based on the binary weighting model with non-binary smoothing
ECIR'03 Proceedings of the 25th European conference on IR research
A study on feature weighting in Chinese text categorization
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
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This paper proposes a multi-dimensional framework for classifying text documents. In this framework, the concept of multidimensional category model is introduced for representing classes. In contrast with traditional flat and hierarchical category models; the multi-dimensional category model classifies each text document in a collection using multiple predefined sets of categories, where each set corresponds to a dimension. Since a multi-dimensional model can be converted to flat and hierarchical models, three classification strategies are possible, i.e., classifying directly based on the multi-dimensional model and classifying with the equivalent flat or hierarchical models. The efficiency of these three classifications is investigated on two data sets. Using k-NN, naïve Bayes and centroid-based classifiers, the experimental results show that the multi-dimensional-based and hierarchical-based classification performs better than the flat-based classifications.