1. A method for generating an enlarged corpus of training entries for a particular application, given a set of k labels and an initial corpus of training m entries, where each of said entries includes at least a data portion, comprising the steps of: for each label l of said k labels, creating an associated rule that specifies one or more conditions that said data portion of an applied entry x must meet in order for said rule to reach a conclusion that said label l attaches to said entry x, and also specifies an confidence measure p(x,l), associated with said conclusion, which measure is a number between 0 and 1; creating an augmented corpus of m training entries, where each entry i in said augmented corpus is created from data portion of entry i in said initial corpus of training entries, i&equals;1,2,... m, with each of said k labels attached to said data portion of said entry i, or not attached to said data portion of said entry i, based on whether a preselected variable Z is either a &plus;1 or a 0, respectively, and with a confidence measure associated with each of said labels being U(x,l)&equals;&lsqb;Z&eegr;p(x,l)&plus;(1−Z)&eegr;(1−p(x,l))&rsqb; when said data portion of said entry i meets said conditions of said rule for label l, &eegr; being a preselected positive number, and being 1−U(x,l) when said data portion of said entry i fails to meet said conditions of said rule for label l; and combining said augmented corpus of m training entries with said initial corpus of training m entries to form said enlarged corpus having 2m training entries.