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        <title>Adapting Topic Models using Lexical Associations with Tree Priors</title>
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    <abstract>Models work best when they are optimized taking into account the evaluation criteria that people care about. For topic models, people often care about interpretability, which can be approximated using measures of lexical association. We integrate lexical association into topic optimization using tree priors, which provide a flexible framework that can take advantage of both first order word associations and the higher-order associations captured by word embeddings. Tree priors improve topic interpretability without hurting extrinsic performance.</abstract>
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%0 Conference Proceedings
%T Adapting Topic Models using Lexical Associations with Tree Priors
%A Yang, Weiwei
%A Boyd-Graber, Jordan
%A Resnik, Philip
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F yang-etal-2017-adapting
%X Models work best when they are optimized taking into account the evaluation criteria that people care about. For topic models, people often care about interpretability, which can be approximated using measures of lexical association. We integrate lexical association into topic optimization using tree priors, which provide a flexible framework that can take advantage of both first order word associations and the higher-order associations captured by word embeddings. Tree priors improve topic interpretability without hurting extrinsic performance.
%R 10.18653/v1/D17-1203
%U https://aclanthology.org/D17-1203
%U https://doi.org/10.18653/v1/D17-1203
%P 1901-1906

Markdown (Informal)

[Adapting Topic Models using Lexical Associations with Tree Priors](https://aclanthology.org/D17-1203) (Yang et al., EMNLP 2017)

• Adapting Topic Models using Lexical Associations with Tree Priors (Yang et al., EMNLP 2017)

ACL

• Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. 2017. Adapting Topic Models using Lexical Associations with Tree Priors. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1901–1906, Copenhagen, Denmark. Association for Computational Linguistics.