Improving Text Models with Latent Feature Vector Representations
首发时间:2019-04-02
Abstract:Probabilistic topic models are widely used to discover potential topics in a collection of documents, while latent feature vector representations have been used to achieve high performance in many NLP tasks. In this paper, we first make document topic vector representations by combining LDA and Topic2Vec, and then we perform document representations based on the topic vectors and the document vectors obtained through Doc2Vec training. Experimental results show that our new model has produced significant improvements in topic consistency and document classification tasks.
keywords: text representation LDA Topic2Vec topic model text categorization
点击查看论文中文信息
基金:
引用
No.****
同行评议
共计0人参与
勘误表
基于潜在特征向量的文本表示模型改进
评论
全部评论0/1000