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        <title>Sprite: Generalizing Topic Models with Structured Priors</title>
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    <abstract>We introduce Sprite, a family of topic models that incorporates structure into model priors as a function of underlying components. The structured priors can be constrained to model topic hierarchies, factorizations, correlations, and supervision, allowing Sprite to be tailored to particular settings. We demonstrate this flexibility by constructing a Sprite-based model to jointly infer topic hierarchies and author perspective, which we apply to corpora of political debates and online reviews. We show that the model learns intuitive topics, outperforming several other topic models at predictive tasks.</abstract>
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%0 Journal Article
%T Sprite: Generalizing Topic Models with Structured Priors
%A Paul, Michael J.
%A Dredze, Mark
%J Transactions of the Association for Computational Linguistics
%D 2015
%V 3
%I MIT Press
%C Cambridge, MA
%F paul-dredze-2015-sprite
%X We introduce Sprite, a family of topic models that incorporates structure into model priors as a function of underlying components. The structured priors can be constrained to model topic hierarchies, factorizations, correlations, and supervision, allowing Sprite to be tailored to particular settings. We demonstrate this flexibility by constructing a Sprite-based model to jointly infer topic hierarchies and author perspective, which we apply to corpora of political debates and online reviews. We show that the model learns intuitive topics, outperforming several other topic models at predictive tasks.
%R 10.1162/tacl_a_00121
%U https://aclanthology.org/Q15-1004
%U https://doi.org/10.1162/tacl_a_00121
%P 43-57

Markdown (Informal)

[Sprite: Generalizing Topic Models with Structured Priors](https://aclanthology.org/Q15-1004) (Paul & Dredze, TACL 2015)

• Sprite: Generalizing Topic Models with Structured Priors (Paul & Dredze, TACL 2015)

ACL

• Michael J. Paul and Mark Dredze. 2015. Sprite: Generalizing Topic Models with Structured Priors. Transactions of the Association for Computational Linguistics, 3:43–57.