Experiments with learning graphical models on text


A rich variety of models are now in use for unsupervised modelling of text documents, and, in particular, a rich variety of graphical models exist, with and without latent variables. To date, there is inadequate understanding about the comparative performance of these, partly because they are subtly different, and they have been proposed and evaluated in different contexts. This paper reports on our experiments with a representative set of state of the art models: chordal graphs, matrix factorisation, and hierarchical latent tree models. For the chordal graphs, we use different scoring functions. For matrix factorisation models, we use different hierarchical priors, asymmetric priors on components. We use Boolean matrix factorisation rather than topic models, so we can do comparable evaluations. The experiments perform a number of evaluations: probability for each document, omni-directional prediction which predicts different variables, and anomaly detection. We find that matrix factorisation performed well at anomaly detection but poorly on the prediction task. Chordal graph learning performed the best generally, and probably due to its lower bias, often out-performed hierarchical latent trees.

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Author information



Corresponding author

Correspondence to Wray Buntine.

Additional information

J. Capdevila is supported by Obra Social “la Caixa”; Dr F. Petitjean is the recipient of an Australian Research Council Discovery Early Career Award (project number DE170100037) funded by the Australian Government.

Communicated by Brandon Malone and Joe Suzuki.


A Log-likelihood


B Omni-directional prediction


C Anomaly detection

See Figs. 9 and 10.

Fig. 9

Anomaly detection in WS data set

Fig. 10

Anomaly detection in 20Newsgroups data set

D Running times

See Figs. 11 and 12.

Fig. 11

Running time in WS data set

Fig. 12

Running time in 20Newsgroups data set

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Capdevila, J., Zhao, H., Petitjean, F. et al. Experiments with learning graphical models on text. Behaviormetrika 45, 363–387 (2018). https://doi.org/10.1007/s41237-018-0050-3

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  • Graphical models
  • Document analysis
  • Unsupervised learning
  • Matrix factorisation
  • Latent variables
  • Evaluation