面向LDA主题模型的文本分类研究进展与趋势

Research Progress and Trend of Text Classification for LDA Topic Model

文本分类是自然语言处理领域的一个重要研究方向.综合分析发现,文本分类的研究和分析,有助于对信息进行有效的分类和管理,并为自然语言处理的应用提供有力的支持.然而,已有的研究在理论和方法层面虽然已经取得了一定的成就,但是文本分类研究涉及内容、领域和技术等多个方面,各学科研究错综复杂,因此还有很多缺陷和不足,需要进一步进行系统和深入的研究.本文针对文本分类这一研究内容,探讨了文本分类和LDA主题模型的相关理论;然后,从技术、方法和应用三个方面分析了面向LDA主题模型的文本分类的研究现状,总结了目前研究中存在的一些问题和研究策略;最后,归纳出文本分类未来的一些发展趋势.

Text classification is an important research direction in the field of natural language processing. It is found that the research and analysis of text classification can help to classify and manage the information effectively and provide strong support for the application of natural language processing. The existing research has made some achievements at the theoretical and methodological level. Nevertheless, the text classification research involves many aspects such as content, domain, and technology, while the research of each subject is complicated. Therefore, there are many defects and shortcomings, which need further systematic and in-depth research. In this paper, we discuss the related theories of text categorization and Latent Dirichlet Allocation（LDA） topic model for the research of text categorization. Then, we analyze the research status of text classification for LDA topic model from three aspects： technology, method, and application. Some problems and research strategies are presented as well. Finally, future trends of text categorization are summarized.