1. A non-transitory computer-readable medium encoding instructions which, when executed by a computer system, cause the computer system to: parse an electronic text document to generate a document vector for the electronic text document, wherein the document vector includes a feature count component and a feature position component, wherein the feature count component includes a plurality of feature count indicators for the electronic text document, wherein the feature position component includes a data structure selected from a group consisting of an ordered list and a tree of document substructure indicators, each document substructure indicator denoting a type of substructure in the electronic text document, and wherein a position of said each document substructure indicator in the data structure characterizes a position of a corresponding substructure in the electronic text document; determine a plurality of composite hyperspace distances between the document vector and a plurality of reference vectors, each composite hyperspace distance being defined between the document vector and a reference vector of the plurality of reference vectors, wherein each composite hyperspace distance is a function of a Euclidean-space distance dependent on the feature count component of the document vector and of an edit distance dependent on the feature position component of the document vector; and classify the electronic text document according to at least one of the plurality of composite hyperspace distances.
1. A non-transitory computer-readable medium encoding instructions which, when executed by a computer system, cause the computer system to: parse an electronic text document to generate a document vector for the electronic text document, wherein the document vector includes a feature count component and a feature position component, wherein the feature count component includes a plurality of feature count indicators for the electronic text document, wherein the feature position component includes a data structure selected from a group consisting of an ordered list and a tree of document substructure indicators, each document substructure indicator denoting a type of substructure in the electronic text document, and wherein a position of said each document substructure indicator in the data structure characterizes a position of a corresponding substructure in the electronic text document; determine a plurality of composite hyperspace distances between the document vector and a plurality of reference vectors, each composite hyperspace distance being defined between the document vector and a reference vector of the plurality of reference vectors, wherein each composite hyperspace distance is a function of a Euclidean-space distance dependent on the feature count component of the document vector and of an edit distance dependent on the feature position component of the document vector; and classify the electronic text document according to at least one of the plurality of composite hyperspace distances. 4. The computer-readable medium of claim 1 , wherein the plurality of feature count indicators comprises at least one indicator selected from a group consisting of a size of the electronic text document, a number of new lines, a number of blank lines, a number of links, and a number of email addresses in the electronic text document.