摘要：

利用K-最近距离算法对哈萨克语文本进行分类，通过统计词频信息和语言信息相结合的方法选择特征，实现一个哈萨克语文本分类系统。在计算特征权重值时不仅考虑词频，还利用特征的集中度、分散度，经过训练和统计对每一类哈萨克语文本形成特征的权重向量，根据K-最近距离算法判断测试文本的所属类别，实验结果表明该方法可行。

关键词: 文本分类, K-最近距离, 集中度, 分散度

Abstract:

The K-nearest-neighbor algorithm is adopted in the classification of the Kazakh text, while in characters chosen, a method that integrates language information and statistical information from the training corpus is applied. The weight of these characters is computed from three parameters: word frequency, centralized degree, decentralized degree. After training, the vector space model of the Kazakh text categorization is got, and the Kazakh text through K-nearest-neighbor algorithm is classified. Experimental results show that this method is feasible.

Key words: text categorization, K-nearest-neighbor, centralized degree, decentralized degree

中图分类号: 

• TP18