A computer-based method for generating a trained knowledge base for the binary classification of text units in a document as either a specified type of text unit or not the specified type of text unit, using a computer, wherein the text units are divisible into terms and tokens, and wherein the text units have identifiable features expressible as numeric values, one of the identifiable features being an average Z value, the method characterized by comprising the steps of: inputting to the computer an initialized knowledge base ; inputting to the computer a training set of text units, each text unit in the training set already being correctly classified as either the specified type of text unit or not the specified type of text unit ; randomly partitioning the inputted training set into a regression subset and a calibration subset ; adding tokens to the text units of the regression subset, wherein each token comprises a name given to a group of terms that matches a specified regular expression so as to label like terms of each group ; removing characters which are not word characters from the text units of the regression subset such that each of the text units of the regression subset comprises only terms and/or tokens separated by spaces ; generating Z values for all terms and tokens in the text units of the regression subset ; using the generated Z values to calculate an average Z value for each of the text units of the regression subset ; generating numeric values for the other features for each text unit of the regression subset ; creating a linear equation for calculating a score for previously unclassified text units in a document, using logistic regression, wherein the linear equation includes the average Z value and the numeric values of selected other features as variables ; establishing a threshold against which each score is to be evaluated, using the generated Z values for each term or token in the regression subset, the linear equation, and the calibration subset ; and inputting the linear equation, the threshold, and the generated Z values for each term or token in the regression subset into the initialized knowledge base to generate the trained knowledge base. inputting to the computer an initialized knowledge base ; inputting to the computer a training set of text units, each text unit in the training set already being correctly classified as either the specified type of text unit or not the specified type of text unit ; randomly partitioning the inputted training set into a regression subset and a calibration subset ; adding tokens to the text units of the regression subset, wherein each token comprises a name given to a group of terms that matches a specified regular expression so as to label like terms of each group ; removing characters which are not word characters from the text units of the regression subset such that each of the text units of the regression subset comprises only terms and/or tokens separated by spaces ; generating Z values for all terms and tokens in the text units of the regression subset ; using the generated Z values to calculate an average Z value for each of the text units of the regression subset ; generating numeric values for the other features for each text unit of the regression subset ; creating a linear equation for calculating a score for previously unclassified text units in a document, using logistic regression, wherein the linear equation includes the average Z value and the numeric values of selected other features as variables ; establishing a threshold against which each score is to be evaluated, using the generated Z values for each term or token in the regression subset, the linear equation, and the calibration subset ; and inputting the linear equation, the threshold, and the generated Z values for each term or token in the regression subset into the initialized knowledge base to generate the trained knowledge base. <EOS>