关键词: 文本数据, 概率主题模型, 隐含狄利克雷分配, 受限玻尔兹曼机

Abstract: With the advent of the era of big data, it has become the focus of scholars how to effectively explore and analyze the topic characteristics from a large amount of text data. Latent Dirichlet allocation as a classical probability theme model is widely used because of its superior text analysis ability. However, most of the models exist in the form of directed graphs which contain implicit subject variables. The distributed representation method defines the semantic distribution of documents in a variety of topics. Moreover, the traditional unsupervised feature extraction model can not deal with the category tagged document data effectively. Therefore, this paper combines with the distributed characteristics of text topic based on the research of restricted Bolzmann machine. Finally it proposes a semi-supervised distributed topic feature extraction model called NRBM which can effectively use the multi-tag information in the document. Finally, its superiority is proved by comparing with the standard LDA topic model.

Key words: text data, probabilistic topic model, latent Dirichlet allocation, restricted Bolzmann machine