山东理工大学学报：自然科学版2012，Vol.26Issue(1)：1-4,4.
基于半监督学习的短文本分类方法
Short text classification based on semi-supervised learning
摘要
Abstract
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.关键词
半监督学习/协作分类/短文本分类/数据挖掘Key words
semi-supervised learning/collective classification/short text classification/data mining分类
计算机与自动化引用本文复制引用
孙学琛,高志强,全志斌,施嘉鸿..基于半监督学习的短文本分类方法[J].山东理工大学学报：自然科学版,2012,26(1):1-4,4.基金项目
国家自然科学基金资助项目（60873153 （）
60803061 （）
61170165） （）
