Weak topic correlation across document collections with different numbers of
topics in individual collections presents challenges for existing
cross-collection topic models. This paper introduces two probabilistic topic
models, Correlated LDA (C-LDA) and Correlated HDP (C-HDP). These address
problems that can arise when analyzing large, asymmetric, and potentially
weakly-related collections. Topic correlations in weakly-related collections
typically lie in the tail of the topic distribution, where they would be
overlooked by models unable to fit large numbers of topics. To efficiently
model this long tail for large-scale analysis, our models implement a parallel
sampling algorithm based on the Metropolis-Hastings and alias methods (Yuan et
al., 2015). The models are first evaluated on synthetic data, generated to
simulate various collection-level asymmetries. We then present a case study of
modeling over 300k documents in collections of sciences and humanities research
from JSTOR.

本文介绍了两种概率主题模型：相关 LDA（C-LDA）和相关 HDP（C-HDP），通过采用基于 Metropolis-Hastings 和 alias 方法的并行采样算法，可以有效地模拟大规模的分析并解决分析大、非对称、潜在弱相关集合时可能出现的问题。这些模型在评估测试数据和案例研究中展示了强大的性能。