@inproceedings{1d602931f3184a4dbf3ddad0384adbd4,
title = "News title classification with support from auxiliary long texts",
abstract = "The performance of short text classification is limited due to its intrinsic shortness of sentences which causes the sparseness of vector space model. Traditional classifiers like SVM are extremely sensitive to the features space, thereby making classification performance unsatisfying in short text related applications. It is believed that using external information to help better represent input data would possibly yield satisfying results. In this paper, we target on the problem of news title classification which is an essential and typical member in short text family and propose an approach which employs external information from long text to address the problem the sparseness. Afterwards Restricted Boltzman Machine are utilised to select features and then finally perform classification using Support Vector Machine. The experimental study on Reuters-21578 and Sogou Chinese news corpus has demonstrates the effectiveness of the proposed method.",
author = "Yuanxin Ouyang and Yao Huangfu and Hao Sheng and Zhang Xiong and Yuanxin Ouyang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 21st International Conference on Neural Information Processing, ICONIP 2014 ; Conference date: 03-11-2014 Through 06-11-2014",
year = "2014",
doi = "10.1007/978-3-319-12640-1\_70",
language = "英语",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "581--588",
editor = "Loo, \{Chu Kiong\} and Yap, \{Keem Siah\} and Wong, \{Kok Wai\} and Andrew Teoh and Kaizhu Huang",
booktitle = "Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings",
address = "德国",
}