基于有监督双词主题模型的短文本分类方法OA

In response to the problems of semantic sparsity and ambiguity in short texts,this paper proposes a Supervised Biterm Topic Model(Su-BTM)and applies it to short text classification.Based on the BTM topic model,distribution parameter between topic and category is introduced to identify semantic information between topic and category,accurate mapping between topic and category is established,and a Su-BTM-Gibbs topic sampling method is proposed to sample the implied topics of each word.Comparative experiments are conducted on two datasets of Chinese and English short texts,and the results show that this method has better classification performance compared to classical models.