CAJ | 학술논문

随着万维网的快速普及和发展,Web上出现了大量短文本,如科技文献摘要、微博和电子邮件等.短文本内容短小,相互联系,已标注数据获得困难,导致传统分类方法很难取得较高的分类精度.为了解决短文本分类问题,提出了一种基于半监督学习的迭代分类算法（SS-ICA）.它使用较少的已标记数据,利用短文本间的关系进行迭代分类.通过与常用分类方法进行对比表明,在标注数据较少的情况下SS-ICA比其他分类器有更高的分类精度.
수착만유망적쾌속보급화발전,Web상출현료대량단문본,여과기문헌적요、미박화전자유건등.단문본내용단소,상호련계,이표주수거획득곤난,도치전통분류방법흔난취득교고적분류정도.위료해결단문본분류문제,제출료일충기우반감독학습적질대분류산법（SS-ICA）.타사용교소적이표기수거,이용단문본간적관계진행질대분류.통과여상용분류방법진행대비표명,재표주수거교소적정황하SS-ICA비기타분류기유경고적분류정도.
With the rapid development of world wide web,there are more and more short texts emerging on the Web,such as abstract of paper,twitter and email.They are short,keeping links with each other,and there are only a small set of labeled instances available.For the sake of classifying the short text,we present a new method named semi-supervised learning-based iterative classification algorithm（SS-ICA）,which has the ability to classify the instances with a small set of labeled instances iteratively.Experiment indicates that SS-ICA significantly increases accuracy when compared to other traditional methods on small training set.