We proposed a novel multilayer correlated topic model (MCTM) to analyze how the main ideas inherit and vary between a document and its different segments, which helps understand an article's structure. The variational expectation-maximization (EM) algorithm was derived to estimate the posterior and parameters in MCTM. We introduced two potential applications of MCTM, including the paragraph-level document analysis and market basket data analysis. The effectiveness of MCTM in understanding the document structure has been verified by the great predictive performance on held-out documents and intuitive visualization. We also showed that MCTM could successfully capture customers' popular shopping patterns in the market basket analysis.

中文翻译：

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多层相关主题模型

我们提出了一种新颖的多层相关主题模型（MCTM），以分析主要思想如何在文档及其不同部分之间继承和变化，从而有助于理解文章的结构。推导了变异期望最大化（EM）算法来估计MCTM中的后验和参数。我们介绍了MCTM的两个潜在应用程序，包括段落级文档分析和市场篮子数据分析。MCTM在理解文档结构方面的有效性已通过对保留文档和直观可视化的出色预测性能得到了验证。我们还表明，MCTM可以在市场购物篮分析中成功捕获客户的流行购物模式。