Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections

Zhang, Jingwei; Gerow, Aaron; Altosaar, Jaan; Evans, James and Jean So, Richard. 2015. 'Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections'. In: Empirical Methods in Natural Language Processing (EMNLP 2015). Lisbon, Portugal 17-21 September 2015.. [Conference or Workshop Item]

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Abstract or Description

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 (CLDA) and Correlated HDP (CHDP). 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.

Item Type:

Conference or Workshop Item (Paper)

Departments, Centres and Research Units:



October 2015Published

Event Location:

Lisbon, Portugal

Date range:

17-21 September 2015.

Item ID:


Date Deposited:

09 Jan 2018 14:48

Last Modified:

29 Apr 2020 16:43


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