基于词性过滤和改进边权重的短文本分类算法

A short text classification algorithm based on part of speech filtering and improved edge weight

针对短文本存在特征稀疏和信息不规范等特点,文中在TextGCN模型的基础上通过增加词性过滤减弱无关词对特征选择的影响,并加入TF-CR算法提高类别无关词权重,最后,通过与几个经典模型进行对比,验证改进模型的有效性。

In view of the features of the short text such as sparse features and non-standard information,this paper,based on the TextGCN model,reduces the influence of irrelevant words on feature selection by adding part of speech filtering,and adds TF-CR algorithm improves the weight of category independent words,and finally verifies the effectiveness of the improved model by comparing with several classical models.