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DOI: 10.14569/IJARAI.2012.010101
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A Method for Chinese Short Text Classification Considering Effective Feature Expansion

Author 1: Mingxuan liu,
Author 2: Xinghua Fan 2

International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 1 Issue 1, 2012.

  • Abstract and Keywords
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Abstract: This paper presents a Chinese short text classification method which considering extended semantic constraints and statistical constraints. This method uses “HowNet” tools to build the attribute set of concept. when coming to the part of feature expansion, we judge the collocation between the attribute words of original text and the characteristics before and after expansion as the semantic constraints, and calculate the ratio between the mutual information of the original contents and the features before expansion versus the mutual information of the original contents and the features after expansion as statistical constraints, so as to judge whether feature expansion is effective with this two constraints , then rationally use various semantic relation word-pairs in short text classification. Experiments show that this method can use semantic relations in Chinese short text classification effectively, and improve the classification performance.

Keywords: component; short text; classification; semantic relations; semantic constraints; statistical constraints; HowNet.

Mingxuan liu, and Xinghua Fan 2. “A Method for Chinese Short Text Classification Considering Effective Feature Expansion”. International Journal of Advanced Research in Artificial Intelligence (IJARAI) 1.1 (2012). http://dx.doi.org/10.14569/IJARAI.2012.010101

@article{liu,2012,
title = {A Method for Chinese Short Text Classification Considering Effective Feature Expansion},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2012.010101},
url = {http://dx.doi.org/10.14569/IJARAI.2012.010101},
year = {2012},
publisher = {The Science and Information Organization},
volume = {1},
number = {1},
author = {Mingxuan liu, and Xinghua Fan 2}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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