@InProceedings{yang-boydgraber-resnik:2017:EMNLP2017,
  author    = {Yang, Weiwei  and  Boyd-Graber, Jordan  and  Resnik, Philip},
  title     = {Adapting Topic Models using Lexical Associations with Tree Priors},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1901--1906},
  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.},
  url       = {https://www.aclweb.org/anthology/D17-1203}
}