@inproceedings{dc0b26462a124098add950240edd37be,
title = "A novel text representation model for text classification",
abstract = "The text representation in text classification is usually a sequence of terms. As the number of terms becomes very high, it is greatly time-consuming to perform existed text categorization tasks. In this paper we presented a novel text representation model for text classification which greatly reduced the required resources. This model represents text with several features. Each feature corresponds to a theme that emerged from a set of related articles. We also introduce an efficient way to build the model. The proposed model has been applied to na{\"i}ve bayes classifier and experiments on Reuters-21578 corpus have shown that the efficiency is greatly improved without sacrificing classification accuracy even when the dimension of the input space is significantly reduced.",
author = "Jun Wang and Yiming Zhou",
year = "2008",
doi = "10.1109/ICINIS.2008.21",
language = "英语",
isbn = "9780769533919",
series = "Proceedings - The 1st International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2008",
pages = "702--705",
booktitle = "Proceedings - The 1st International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2008",
note = "1st International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2008 ; Conference date: 01-11-2008 Through 03-11-2008",
}