Peixian Chen, Nevin L. Zhang, Tengfei Liu, Leonard K.M. Poon, Zhourong Chen, Farhan Khawar, Latent tree models for hierarchical topic detection, Artificial Intelligence, Volume 250, 2017, Pages 105-124, DOI: 10.1016/j.artint.2017.06.004.
We present a novel method for hierarchical topic detection where topics are obtained by clustering documents in multiple ways. Specifically, we model document collections using a class of graphical models called hierarchical latent tree models (HLTMs). The variables at the bottom level of an HLTM are observed binary variables that represent the presence/absence of words in a document. The variables at other levels are binary latent variables that represent word co-occurrence patterns or co-occurrences of such patterns. Each latent variable gives a soft partition of the documents, and document clusters in the partitions are interpreted as topics. Latent variables at high levels of the hierarchy capture long-range word co-occurrence patterns and hence give thematically more general topics, while those at low levels of the hierarchy capture short-range word co-occurrence patterns and give thematically more specific topics. In comparison with LDA-based methods, a key advantage of the new method is that it represents co-occurrence patterns explicitly using model structures. Extensive empirical results show that the new method significantly outperforms the LDA-based methods in term of model quality and meaningfulness of topics and topic hierarchies.