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Maximum Entropy Markov Models for Information Extraction and Segmentation Andrew McCallum Dayne Freitag Just Research, 4616 Henry Street, Pittsburgh, PAUSA
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Document Date: 2003-09-26 13:58:21


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City

Winnow / Pittsburgh / Cambridge / Menlo Park / Laird / /

Company

Solla S. A. / Neural Information Processing Systems / MIT Press / Fernando Pereira AT&T Labs / Russell / AAAI Press / /

Country

Jordan / Canada / /

Facility

Carnegie Mellon University / University of Pennsylvania / /

IndustryTerm

forwardbackward algorithm / comms software developers / dynamic programming solution / simulation algorithms / probabilistic tool / maximum entropy solution / text-related applications / natural language processing / text applications / finite-state networks / model training algorithm / maximium entropy solution / dynamic probabilistic networks / /

Organization

Royal Statistical Society / Artificial Intelligence Montreal / University of Pennsylvania / MIT / Carnegie Mellon University / Pattern Analysis and Machine Intelligence / Association for Computational Linguistics / /

Person

John Lafferty / Andrew McCallum Dayne Freitag / Della Pietra / V / Kamal Nigam / Della Pietra / Morgan Kaufmann / /

Position

model / Environmental Model for Reinforcement Learning / maximumentropy model / conditional Markov model / /

ProvinceOrState

New Jersey / New Mexico / New Brunswick / Pennsylvania / California / Massachusetts / /

PublishedMedium

IEEE Transactions on Pattern Analysis and Machine Intelligence / Computational Linguistics / Journal of the Royal Statistical Society / Machine Learning / /

Technology

maximum entropy Markov model training algorithm / alpha / Baum-Welch algorithms / speech recognition / natural language processing / Stochastic simulation algorithms / Viterbi algorithm / Baum-Welch algorithm / http / Machine Learning / forwardbackward algorithm / EM algorithm / /

URL

http /

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