Fast Inference for Interactive Models of Text

Jeffrey Lund, Paul Felt, Kevin Seppi, Eric Ringger


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.
Anthology ID:
C16-1282
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2997–3006
Language:
URL:
https://aclanthology.org/C16-1282
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Fast Inference for Interactive Models of Text (Lund et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1282.pdf