Given a naive Bayes statistical language model, we construct an NB-R representation that produces |S| boolean features F s (x i ) for each token x i and each possible latent state s ∈ S: For a reasonable choice of S (i.e., |S| |V|), each feature should be observed often in a sufficiently large training data set.
compared with n-GRAM-R, NB-R produces far fewer features.