CAJ | 학술논문

文本特征表示是在文本自动分类中最重要的一个环节。在基于向量空间模型（VSM）的文本表示中特征单元粒度的选择直接影响到文本分类的效果。在维吾尔文文本分类中，对于单词特征不能更好地表征文本内容特征的问题，在分析了维吾尔文Bigram对文本分类作用的基础上，构造了一个新的统计量CHIMI，并在此基础上提出了一种维吾尔语Bigram特征提取算法。将抽取到的Bigram作为文本特征，采用支持向量机（SVM）算法对维吾尔文文本进行了分类实验。实验结果表明，与以词为特征的文本分类相比，Bigram作为文本特征能够提高维吾尔文文本分类的准确率和召回率并且通过实验验证了该算法的有效性。
문본특정표시시재문본자동분류중최중요적일개배절。재기우향량공간모형（VSM）적문본표시중특정단원립도적선택직접영향도문본분류적효과。재유오이문문본분류중，대우단사특정불능경호지표정문본내용특정적문제，재분석료유오이문Bigram대문본분류작용적기출상，구조료일개신적통계량CHIMI，병재차기출상제출료일충유오이어Bigram특정제취산법。장추취도적Bigram작위문본특정，채용지지향량궤（SVM）산법대유오이문문본진행료분류실험。실험결과표명，여이사위특정적문본분류상비，Bigram작위문본특정능구제고유오이문문본분류적준학솔화소회솔병차통과실험험증료해산법적유효성。
Text representation is the most important phase in automatic text categorization. In the vector space model based text representation, the selection of feature granularity has the direct impact on the text categorization performance. The word features don’t have the good representative power to represent the Uyghur texts in text categorization. To solve this problem, the CHIMI based Uyghur Bigram extraction method is proposed and the Uyghur text categorization experi-ments are conducted using support vector machine algorithm based on the extracted Bigrams as text features. The experi-mental results show that the Bigram based Uyghur text categorization achieves higher classification precision and recall compared to the word based categorization and experiments demonstrate the effectiveness of the proposed algorithm.