The key ideas are the modeling of word ngram probabilities with a maximum entropy model, and the use of word-class information in the definition of the features.
each word w is assigned deterministically to a class c, allowing the n-gram probabilities to be estimated as the product of class and word parts Both components are themselves maximum entropy n-gram models in which the probability of a word or class label l given history h is determined by The features f k (h, l) used are the presence of various patterns in the concatenation of hl, for example whether a particular suffix is present in hl.