1. A method comprising: receiving, by at least one data processor, data characterizing each of a plurality of documents within a document set; grouping, by at least one data processor, the plurality of documents into a plurality of stacks using one or more grouping algorithms, wherein key words are identified in each document and weights specified by a scorecard scoring model are assigned to variables corresponding to each key word, wherein a scoring algorithm, using corresponding variables and weights, provides a score for each document which is used by the grouping algorithm when grouping the documents; identifying, by at least one data processor, a prime document for each stack, the prime document including attributes representative of the entire stack; providing, by at least one data processor, data characterizing documents for each stack including at least the identified prime document to at least one human reviewer; receiving, by at least one data processor, user-generated input from the human reviewer categorizing each provided document; providing, by at least one data processor, data characterizing the user-generated input; evaluating, by at least one data processor, identified grouping errors using at least one of Z-test techniques and multiple regression techniques; determining, by at least one data processor based on the evaluating, a relative contribution of the variables used by the grouping algorithm to the grouping errors; and modifying, by at least one data processor, the grouping algorithm so that at least one weight assigned by the grouping algorithm off-sets the relative contribution of the variables used by the grouping algorithm to an error rate, wherein subsequently received documents are grouped using the modified grouping algorithm.
1. A method comprising: receiving, by at least one data processor, data characterizing each of a plurality of documents within a document set; grouping, by at least one data processor, the plurality of documents into a plurality of stacks using one or more grouping algorithms, wherein key words are identified in each document and weights specified by a scorecard scoring model are assigned to variables corresponding to each key word, wherein a scoring algorithm, using corresponding variables and weights, provides a score for each document which is used by the grouping algorithm when grouping the documents; identifying, by at least one data processor, a prime document for each stack, the prime document including attributes representative of the entire stack; providing, by at least one data processor, data characterizing documents for each stack including at least the identified prime document to at least one human reviewer; receiving, by at least one data processor, user-generated input from the human reviewer categorizing each provided document; providing, by at least one data processor, data characterizing the user-generated input; evaluating, by at least one data processor, identified grouping errors using at least one of Z-test techniques and multiple regression techniques; determining, by at least one data processor based on the evaluating, a relative contribution of the variables used by the grouping algorithm to the grouping errors; and modifying, by at least one data processor, the grouping algorithm so that at least one weight assigned by the grouping algorithm off-sets the relative contribution of the variables used by the grouping algorithm to an error rate, wherein subsequently received documents are grouped using the modified grouping algorithm. 15. A method as in claim 1 , wherein the data characterizing documents for each stack provided to the at least one human reviewer comprise digitally scanned versions of such documents.