CAJ | 학술논문

为了实现哈萨克语文本分类，根据哈萨克语语法规则，给出了哈萨克语文本词干的提取方法；结合DFR特征选择方法和VSM文本表示模型实现哈萨克语文本的预处理，提出了一种SVM和修正KNN协同的文本分类算法，分别在自行构建的语料集和整理的《新疆日报》哈萨克语数据集上进行大量文本分类仿真实验。结果表明，该方法在哈萨克语文本分类上具有良好的分类性能，并比SVM ，KNN的测试性能优越。
위료실현합살극어문본분류，근거합살극어어법규칙，급출료합살극어문본사간적제취방법；결합DFR특정선택방법화VSM문본표시모형실현합살극어문본적예처리，제출료일충SVM화수정KNN협동적문본분류산법，분별재자행구건적어료집화정리적《신강일보》합살극어수거집상진행대량문본분류방진실험。결과표명，해방법재합살극어문본분류상구유량호적분류성능，병비SVM ，KNN적측시성능우월。
In order to get the Kazakh language text classification , according to the Kazakh language features , this paper presents the Kazakh stem extract principle , and implementes the Kazakh text preprocessing combined with DFR feature selection and VSM model . This paper proposes a SVM-modified KNN algorithm ,a large number of text categorization experiments are simulated on the own building data sets and the Xinjiang Daily Kazakh data sets respectively . The numerical experiment results show that the method in the Kazakh language text classification has a good classification performance , and its test performance is better than the SVM and KNN .