| 基于K-最近距离的自动文本分类的研究 |
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| 引用本文： | 孙健,王伟,钟义信.基于K-最近距离的自动文本分类的研究[J].北京邮电大学学报,2001,24(1):42-46. |
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| 作者姓名： | 孙健 王伟 钟义信 |
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| 作者单位： | 北京邮电大学信息工程学院, 北京 100876 |
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| 基金项目： | 国家自然科学基金资助项目(69982001) |
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| 摘 要： | 提出并实现了利用统计词频信息和语言信息相结合的方法选择特征,计算特征的权重值时不仅考虑词频,还利用了特征的集中度、分散度.经过训练和统计对每一类文本形成特征的权重向量,利用K-最近距离的方法对测试集进行分类.对英文文本的测试结果表明,该算法提高了文本分类的准确率.
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| 关 键 词： | 自然语言理解 向量空间模型 K-最近距离 自动文本分类 |
| 文章编号： | 1007-5321(2001)01-0042-05 |
| 收稿时间： | 2000-09-14 |
| 修稿时间： | 2000年9月14日 |
Automatic Text Categorization Based on K-Nearest Neighbor |
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| SUN Jian,WANG Wei,ZHONG Yi xin.Automatic Text Categorization Based on K-Nearest Neighbor[J].Journal of Beijing University of Posts and Telecommunications,2001,24(1):42-46. |
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| Authors: | SUN Jian WANG Wei ZHONG Yi xin |
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| Affiliation: | Information Engineering School, Beijing University of Posts and Telecommunications, Beijing 100876, China |
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| Abstract: | A method that integrates language information and statisticalinformation from the training corpus is put forward. The weight of these characters is computed from three parameters: word frequency, centralized degree, decentralized degree. After training, we get the vector space model of the text categorization. The classification of the input text is decided by K -nearest-neighbor.The result shows that the method improves the accuracy of the categorization. |
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| Keywords: | natural language understanding vector space model K-nearest-neighbor automatic text categorization |
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