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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.