Latent tree models for hierarchical topic detection

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 ar...

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Bibliographic Details
Published in:Artificial intelligence 2017-09, Vol.250, p.105-124
Main Authors: Chen, Peixian, Zhang, Nevin L., Liu, Tengfei, Poon, Leonard K.M., Chen, Zhourong, Khawar, Farhan
Format: Article
Language:English
Subjects:
Clustering
Graphs
Hierarchical latent tree analysis
Hierarchical topic detection
Hierarchies
Mathematical models
Partitions
Probabilistic graphical models
Studies
Text analysis
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Summary: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.
ISSN:0004-3702
1872-7921
DOI:10.1016/j.artint.2017.06.004