基于伪相关反馈的短文本扩展与分类

Short text expansion and classification based on pseudo-relevance feedback

针对短文本分类问题,提出基于伪相关反馈(PFR)的短文本扩展与分类方法.在保持语义不变的情况下,利用互联网中的相似语料对短文本的内容进行了扩展.对现有的仅使用局部特征的扩展语料特征抽取方法进行改进,引入全局特征抽取,将全局特征与局部特征相结合得到了更好的特征向量,有效地解决了分类过程中由短文本长度有限导致的特征矩阵高度稀疏的问题.通过在开放数据集上的测试和与其他文献的结果比对,验证了该方法在短文本分类的问题上可以取得较好的效果.

A novel classification method based on pseudo-relevance feedback （PFR） was proposed in order to solve the sparseness problems in short text classification. The short texts were expanded using the web pages which are similar to them in semantic level. The feature vector generation algorithm was modified to extract both the local features and the global features. The method can alleviate the sparseness problem of the final feature matrix, which is common in short text classification because of the limited length of the texts. The experimental results on an open dataset show that the method can significantly improve the short text classification effect compared with state-of-the-art methods.