@inproceedings{DBLP:conf/coling/LundFSR16, booktitle={{COLING} 2016, 26th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, December 11-16, 2016, Osaka, Japan}, crossref={DBLP:conf/coling/2016}, link={http://aclweb.org/anthology/C/C16/C16-1282.pdf}, timestamp={Thu, 15 Dec 2016 16:57:46 +0100}, author={Jeffrey Lund and Paul Felt and Kevin D. Seppi and Eric K. Ringger}, biburl={http://dblp.uni-trier.de/rec/bib/conf/coling/LundFSR16}, 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 MOMRESP, a multinomial crowdsourcing model.}, bibsource={dblp computer science bibliography, http://dblp.org}, year={2016}, title={Fast Inference for Interactive Models of Text}, }