| 文本分类相似度模型和概率模型的实现与比较* |
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| 引用本文： | 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 |
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| 作者姓名： | 刘华 |
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| 作者单位： | 暨南大学华文学院,广州,510610 |
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| 基金项目： | 教育部"国家语言资源监测"项目 |
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| 摘 要： | 设计并建立一个基于向量空间模型和简单贝叶斯的文本分类系统，系统引入小类校正和兼类判断的算法，完成层级多标签的分类。进行基于向量空间模型和简单贝叶斯分类效果的对比，实验证明，在约3万篇测试集上（共15个大类，244个小类），基于向量空间模型的大类分类高25.2个百分点，层级小类分类高26.3个百分点。
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| 关 键 词： | 向量空间模型 简单贝叶斯 |
| 收稿时间： | 2006-01-12 |
| 修稿时间： | 2006-01-12 |
Implementation and Comparison of Similarity and Probabilistic Mode in Text Categorization |
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| Liu Hua. Implementation and Comparison of Similarity and Probabilistic Mode in Text Categorization[J]. New Technology of Library and Information Service, 2006, 1(4): 53-55 |
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| Authors: | Liu Hua |
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| Affiliation: | College of Chinese Language and Culture of Jinan University, Guangzhou 510610, China |
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| Abstract: | 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 sub-category,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. |
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| Keywords: | Text categorization Vector space model Naive-Bayes |
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