计算机应用研究2017，Vol.34Issue(7)：2055-2058,4.DOI:10.3969/j.issn.1001-3695.2017.07.029
融合词向量特征的双词主题模型
Biterm topic model with word vector features
摘要
Abstract
To solve the problem of content sparsity and lack of context information existed inherently in short texts,this paper proposed a biterm topic model (BTM) incorporating word vector features LF-BTM based on BTM.This model introuded latent feature model which utilized its abundant word vector information to offset the data sparsity.Generation of words in each biterm was influenced jointly by topic-word multinomial distribution and latent features model in the improved generative process.Parameters in the model could be learned by of Gibbs sampling method.Experimental results on real-world short texts datasets demonstrate that the model can integrate word vectors trained from external general large-scale corpora to produce significant improvements on topic coherence.关键词
主题模型/潜在狄利克雷分配/短文本/双词主题模型/词向量/吉布斯采样Key words
topic model/latent Dirichlet allocation/short texts/biterm topic model/word vector/Gibbs sampling分类
信息技术与安全科学引用本文复制引用
刘良选,黄梦醒..融合词向量特征的双词主题模型[J].计算机应用研究,2017,34(7):2055-2058,4.基金项目
国家自然科学基金资助项目(61462022) （61462022）
