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The principle of maximum entropy states that, subject to precisely stated prior data (such as a proposition that expresses testable information), the probability distribution which best represents the current state of knowledge is the one with largest entropy.
Please cite the following paper when you use this extension in your research: The Max Ent model will be saved in a dedicated format to a file gz ('hme' stands for hierarchical Maximum Entropy).
All N-grams up to the specified order in the training data will be used as features.
General Editors: David Bourget (Western Ontario) David Chalmers (ANU, NYU) Area Editors: David Bourget Gwen Bradford Berit Brogaard Margaret Cameron David Chalmers James Chase Rafael De Clercq Ezio Di Nucci Barry Hallen Hans Halvorson Jonathan Jenkins Ichikawa Michelle KoschØystein Linnebo Jee Loo Liu Paul Livingston Brandon Look Manolo Martínez Matthew Mc Grath Michiru Nagatsu Susana Nuccetelli Gualtiero Piccinini Giuseppe Primiero Jack Alan Reynolds Darrell Rowbottom Aleksandra Samonek Constantine Sandis Howard Sankey Jonathan Schaffer Thomas Senor Robin Smith Daniel Star Jussi Suikkanen Lynne Tirrell Aness Webster Other editors Contact us Learn more about Phil Papers Jeffrey conditionalization is generalized to the case in which new evidence bounds the possible revisions of a prior below by a Dempsterian lower probability.
This patch adds the functionality to train and apply maximum entropy (Max Ent) language models to the SRILM toolkit. As of SRILM 1.7.1, the extension is included in the main SRILM distribution – no patching is necessary.