Bayesian nonparametric learning for complicated text mining

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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 text mining tasks. To fill this gap, this research aims to develop a set of Bayesian nonparametric models to accomplish four selected complex text mining tasks. First, three Bayesian nonparametric sparse nonnegative matrix factorization models, based on two innovative dependent Indian buffet processes, are proposed for document-word co-clustering tasks. Second, a Dirichlet mixture probability measure strategy is proposed to link the topics from different layers, and is used to build a Bayesian nonparametric deep topic model for topic hierarchy learning. Third, the thesis develops a Bayesian nonparametric relational topic model for document network learning tasks by a subsampling Markov random field. Lastly, the thesis develops Bayesian nonparametric cooperative hierarchical structure models for multi-label document learning task based on two stochastic process operations: inheritance and cooperation. The findings of this research not only contribute to the development of Bayesian nonparametric learning theory, but also provide a set of effective tools for complicated text mining applications.
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