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Text Classification Improved through Automatically Extracted Sequences

Dou Shen, Jian-Tao Sun, Qiang Yang, Hui Zhao, and Zheng Chen

Abstract

We propose to use the n-multigram model to help the automatic text classification task. This model could automatically discover the latent semantic sequences contained in the document set of each category. Based on the n-multigram model and the n-gram language model, we put forward two text classification algorithms. The experiments on RCV1 show that our proposed algorithm based on n-multigram model can achieve the similar classification performance compared with the one based on n-gram model. However, the model size of our algorithm is only 4.21% of the latter one. Another proposed algorithm based on the combination of nmultigram model and n-gram model improves the micro- F1 and macro-F1 values by 3.5% and 4.5% respectively which support the validity of our approach.

Details

Publication typeInproceedings
Published inICDE '06: Proceedings of the 22nd International Conference on Data Engineering
URLhttp://dx.doi.org/10.1109/ICDE.2006.158
Pages121
ISBN0-7695-2570-9
AddressWashington, DC, USA
PublisherIEEE Computer Society
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