Bayesian nonparametric learning for complicated text mining

Text mining has gained the ever-increasing attention of researchers in recent years because text is one of the most natural and easy ways to express human knowledge and opinions, and is therefore believed to have a variety of application scenarios and a potentially high commercial value. It is commonly accepted that Bayesian models with finite-dimensional probability distributions as building blocks, also known as parametric topic models, are effective tools for text mining. However, one problem in existing parametric topic models is that the hidden topic number needs to be fixed in advance. Determining an appropriate number is very difficult, and sometimes unrealistic, for many real-world applications and may lead to over-fitting or under-fitting issues. Bayesian nonparametric learning is a key approach for learning the number of mixtures in a mixture model (also called the model selection problem), and has emerged as an elegant way to handle a flexible number of topics. The core idea of Bayesian nonparametric models is to use stochastic processes as building blocks, instead of traditional fixed-dimensional probability distributions. Even though Bayesian nonparametric learning has gained considerable research attention and undergone rapid development, its ability to conduct complicated text mining tasks, such as: document-word co-clustering, document network learning, multi-label document learning, and so on, is still weak. Therefore, there is still a gap between the Bayesian nonparametric learning theory and complicated real-world