1. A computer-executed method for classifying a target document in the form of a digitally encoded natural-language text into one or more of two or more different classes, comprising the steps of: (a) for each of a plurality of terms selected from one of (i) non-generic words in the document, (ii) proximately arranged word groups in the document, and (iii) a combination of (i) and (ii), determining a selectivity value calculated as the frequency of occurrence of that term in a library of texts in one field, relative to the frequency of occurrence of the same term in one or more other libraries of texts in one or more other fields, respectively, and (b) representing the document as a vector of terms, where the coefficient assigned to each term is a function of the selectivity value determined for that term. (c) determining for each of a plurality of sample texts, a match score related to the number of descriptive terms present in or derived from that text that match those in the target document, where each of the plurality of sample texts has an associated classification identifier that identifies the one of more different classes to which that text belongs, (d) selecting one or more of the sample texts having the highest match scores, (e) recording the one or more classification identifiers associated with the one or more sample texts having the highest match scores, and (f) associating the one or more classification identifiers from step (e) with the target document, thereby to classify the target document as belonging to one or more classes represented by at least one of the classification identifiers from step (e).