现代信息科技2024，Vol.8Issue(10)：56-59,4.DOI:10.19850/j.cnki.2096-4706.2024.10.012
基于有监督双词主题模型的短文本分类方法
A Short Text Classification Method Based on Supervised Biterm Topic Model
摘要
Abstract
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.关键词
语义稀疏/BTM主题模型/隐含主题/短文本分类Key words
semantic sparsity/BTM topic model/implied topic/short text classification分类
信息技术与安全科学引用本文复制引用
卫红敏..基于有监督双词主题模型的短文本分类方法[J].现代信息科技,2024,8(10):56-59,4.基金项目
2022年山东华宇工学院科技计划项目(2022KJ13) （2022KJ13）
