A Multilayer Correlated Topic Model
Abstract
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.
Cite
@article{arxiv.2101.02028,
title = {A Multilayer Correlated Topic Model},
author = {Ye Tian},
journal= {arXiv preprint arXiv:2101.02028},
year = {2021}
}
Comments
11 pages, 4 figures