1. A method of constructing a statistical classifier that classifies sentences having words into classes, comprising the steps of: receiving labeled training data comprising text sentences labeled by class; receiving unlabeled test data T; calculating an initial class probability parameter θ y values for each class y based on the number of training data sentences having the corresponding class label; constructing a set of binary valued feature vectors for sentences in the training data, each set of feature vectors corresponding to a class label, each feature vector corresponding to a sentence, each feature corresponding to a word k; calculating initial word/class probability parameter θ ky values based on training data feature vectors for each class y; and calculating initial word/class improbability parameter {overscore (θ)} ky values for the training data where {overscore (θ)} ky =1−θ ky .