@inproceedings{lund-etal-2016-fast,
title = "Fast Inference for Interactive Models of Text",
author = "Lund, Jeffrey and
Felt, Paul and
Seppi, Kevin and
Ringger, Eric",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1282",
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.",
}
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%0 Conference Proceedings
%T Fast Inference for Interactive Models of Text
%A Lund, Jeffrey
%A Felt, Paul
%A Seppi, Kevin
%A Ringger, Eric
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F lund-etal-2016-fast
%X 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.
%U https://aclanthology.org/C16-1282
%P 2997-3006
Markdown (Informal)
[Fast Inference for Interactive Models of Text](https://aclanthology.org/C16-1282) (Lund et al., COLING 2016)
ACL
- Jeffrey Lund, Paul Felt, Kevin Seppi, and Eric Ringger. 2016. Fast Inference for Interactive Models of Text. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2997–3006, Osaka, Japan. The COLING 2016 Organizing Committee.