Foundations of statistical natural language processing
Foundations of statistical natural language processing
Restructuring sparse high dimensional data for effective retrieval
Proceedings of the 1998 conference on Advances in neural information processing systems II
Toward Multi-modal Music Emotion Classification
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
PCA document reconstruction for email classification
Computational Statistics & Data Analysis
Distributional term representations for short-text categorization
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Improving short text classification using public search engines
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
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Many applications, such as word-sense disambiguation and information retrieval, can benefit from text classification. Text classifiers based on Independent Component Analysis (ICA) try to make the most of the independent components of text documents and give in many cases good classification effects. Short-text documents, however, usually have little overlap in their feature terms and, in this case, ICA can not work well. Our aim is to solve the short-text problem in text classification by using Latent Semantic Analysis (LSA) as a data preprocessing method, then employing ICA for the preprocessed data. The experiment shows that using ICA and LSA together rather than only using ICA in Chinese short-text classification can provide better classification effects.