1. A method of constructing a statistical classifier that classifies sentences having words into classes, comprising: receiving, in memory accessible by a computer processor, labeled training data comprising text sentences labeled by class; calculating, using the computer processor, an initial class probability parameter θ y value 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, using the computer processor, initial word/class probability parameter θ ky values based on training data feature vectors for each class y; calculating, using the computer processor, initial word/class improbability parameter θ ky values for the training data where θ ky =1−θ ky ; and constructing and outputting a statistical classifier of parameterized probability values of all classes jointly given sentence x, and wherein constructing a statistical classifier comprises iteratively re-estimating initial parameters θ ky , θ ky without recalculating θ y , using the computer processor.
5. The method of claim 1 , and wherein constructing binary valued feature vectors comprises: accessing a word lexicon to determine whether words are in a lexicon; and constructing each feature vector based on assigning values of 1 and 0 to words in the corresponding sentence based on whether the feature words are in or not in the lexicon, respectively.