@InProceedings{lund-EtAl:2016:COLING,
  author    = {Lund, Jeffrey  and  Felt, Paul  and  Seppi, Kevin  and  Ringger, Eric},
  title     = {Fast Inference for Interactive Models of Text},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2997--3006},
  abstract  = {Probabilistic models are a useful means for analyzing large text corpora.
	Integrating such models with human interaction enables many new use cases.
	However, adding human interaction to probabilistic models requires inference
	algorithms which are both fast and accurate. We explore the use of Iterated
	Conditional Modes as a fast alternative to Gibbs sampling or variational EM. We
	demonstrate superior performance both in run time and model quality on three
	different models of text including a DP Mixture of Multinomials for web search
	result clustering, the Interactive Topic Model, and M OM R ESP , a multinomial
	crowdsourcing model.},
  url       = {http://aclweb.org/anthology/C16-1282}
}