This paper has implemented a text categorization system based on Vector Space Model(VSM) and Naive-Bayes(NB). When estimating the category, the authors enhance the veracity of parent-category by emendation of subcategory, and judge whether document has multi-classification and multi-label by estimating the similar difference of classifier’s final values. The experiment proves that VSM is better than NB in text representation: MicroF1 increases of 25.2 percent of parent-category, and MicroF1 increases of 26.3 percent of sub-category.
刘华 . 文本分类相似度模型和概率模型的实现与比较*[J]. 现代图书情报技术, 2006, 1(4): 53-55.
Liu Hua . Implementation and Comparison of Similarity and Probabilistic Mode in Text Categorization. New Technology of Library and Information Service, 2006, 1(4): 53-55.
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