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How to Turn a Bayesian into a Markovian
change the feature generator from single words to spanning multiple words *
Change the weighting so that longer features have more weight (ie. Longer features generate local probabilities closer to 0.0 and 1.0)
The 22n weighting means that the weights were 1, 4, 16, 64, 256, ... for span lengths of 1, 2, 3, 4, 5 ... words
* Rohan Malkhare at USF has a very nice extension of this to a statistical model of an entire message..... he has been advised to publish As Soon As Possible.