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Optimization for Vietnamese text classification problem by reducing features set

机译:减少功能集越南文本分类问题的优化

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Vietnamese is the single syllable language, so that process of word segmentation is relatively complex, if split word based on whitespaces, it is not accuracy, on the other hand Vietnamese segmentation tools are not high effective. In this paper, we propose a new method that used only topic word for calculating to increase accuracy of the Vietnameses text classification system and optimize the process of calculating. The experimental results show that our method more effective than the proposed approach, higher accuracy and reduce the computational complexity.
机译:越南语是单个音节语言,使词分割的过程相对复杂,如果基于空白的拆分字,则不准确,另一方面越南分段工具不高效。在本文中,我们提出了一种新方法,这些方法仅用于计算越南文本分类系统的准确性,并优化计算过程。实验结果表明,我们的方法比提出的方法更有效,更高的准确性,降低计算复杂性。

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