Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
On feature distributional clustering for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Distributional word clusters vs. words for text categorization
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Can chinese web pages be classified with english data source?
Proceedings of the 17th international conference on World Wide Web
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Automatic word clustering for text categorization using global information
AIRS'04 Proceedings of the 2004 international conference on Asian Information Retrieval Technology
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In this paper, we propose an efficient text classification method using term projection. Firstly, we use a modified *** 2 statistic to project terms into predefined categories, which is more efficient compared to other clustering methods. Afterwards, we utilize the generated clusters as features to represent the documents. The classification is then performed in a rule-based manner or via SVM. Experiment results show that our modified *** 2 statistic feature selection method outperforms traditional *** 2 statistic especially at lower dimensionalities. And our method is also more efficient than Latent Semantic Analysis (LSA) on homogeneous dataset. Meanwhile, we can reduce the feature dimensionality by three orders of magnitude to save training and testing cost, and maintain comparable accuracy. Moreover, we could use a small training set to gain an approximately 4.3% improvement on heterogeneous dataset as compared to traditional method, which indicates that our method has better generalization capability.