CAJ | 학술논문

以短文本为主体的微博等社交媒体，因具备文本短、特征稀疏等特性，使得传统文本分类方法不能够高精度地对短文本进行分类。针对这一问题，文章提出了基于词项关联的短文本分类方法。首先对训练集进行强关联规则挖掘，将强关联规则加入到短文本的特征中，提高短文本特征密度，进而提高短文本分类精度。对比实验表明，该方法一定程度上减缓了短文本特征稀疏特点对分类结果的影响，提高了分类准确率、召回率和F1值。
이단문본위주체적미박등사교매체，인구비문본단、특정희소등특성，사득전통문본분류방법불능구고정도지대단문본진행분류。침대저일문제，문장제출료기우사항관련적단문본분류방법。수선대훈련집진행강관련규칙알굴，장강관련규칙가입도단문본적특정중，제고단문본특정밀도，진이제고단문본분류정도。대비실험표명，해방법일정정도상감완료단문본특정희소특점대분류결과적영향，제고료분류준학솔、소회솔화F1치。
Due to its characteristics of shortness and sparseness, short text, as the main body of microblog and other social media, cannot be accurately classiifed by the traditional text classiifcation methods. To solve this problem, a method of short text classiifcation based on association rules of lexical items was proposed in this paper. Firstly, the training set based on the strong association rules was mined, and then the strong association rules was added to the features of short text so as to increase the feature density of short text, thereby to increase the accuracy of results of short text classiifcation. Comparative experiments show that this method, to some extent, reduces the impact of sparseness of short text on the classiifcation results, and it improves the classiifcation accuracy, recall values andF1 values.