  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.
