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A Text Categorization Algorithm Based on Sense Group

机译:基于感知群的文本分类算法

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摘要

Giving further consideration on linguistic feature, this study proposes an algorithm of Chinese text categorization based on sense group. The algorithm extracts sense group by analyzing syntactic and semantic properties of Chinese texts and builds the category sense group library. SVM is used for the experiment of text categorization. The experimental results show that the precision and recall of the new algorithm based on sense group is better than that of traditional algorithms.
机译:进一步考虑语言特征,提出了一种基于感觉群的中文文本分类算法。该算法通过分析中文文本的句法和语义特性提取词义组,并建立类别词义组库。 SVM用于文本分类的实验。实验结果表明,基于感知群的新算法的精度和召回率均优于传统算法。

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