A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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ICML '05 Proceedings of the 22nd international conference on Machine learning
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ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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Introduction to Information Retrieval
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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This paper compares statistical techniques for text classification using Naïve Bayes and Support Vector Machines, in context of Urdu language. A large corpus is used for training and testing purpose of the classifiers. However, those classifiers cannot directly interpret the raw dataset, so language specific preprocessing techniques are applied on it to generate a standardized and reduced-feature lexicon. Urdu language is morphological rich language which makes those tasks complex. Statistical characteristics of corpus and lexicon are measured which show satisfactory results of text preprocessing module. The empirical results show that Support Vector Machines outperform Naïve Bayes classifier in terms of classification accuracy.