20. The method of claim 19 , wherein the at least one training token comprises a plurality of training tokens and wherein the step of generating the Boolean query for the cluster in the plurality of clusters further comprises the steps of: f. calculating a training token/cluster weight matrix for the plurality of training tokens for the cluster; g. ranking the plurality of training tokens for the cluster according to the matrix; and h. selecting a list of the top N training tokens from the ranked plurality of training tokens for the cluster, where N is a positive integer.
20. The method of claim 19 , wherein the at least one training token comprises a plurality of training tokens and wherein the step of generating the Boolean query for the cluster in the plurality of clusters further comprises the steps of: f. calculating a training token/cluster weight matrix for the plurality of training tokens for the cluster; g. ranking the plurality of training tokens for the cluster according to the matrix; and h. selecting a list of the top N training tokens from the ranked plurality of training tokens for the cluster, where N is a positive integer. 22. The method of claim 20 , wherein generating the Boolean query further comprises the steps of: i. selecting a next best positive training token from the top N training tokens; j. determining if whether a recall improvement is greater than a minimum improvement; k. responsive to the recall improvement not being greater than the minimum improvement, going to step (m); l. responsive to the recall improvement being greater than the minimum improvement, setting a query equal to the query ORed with the selected next best positive training token; m. determining whether a number of OR tokens is less than a maximum number of OR tokens; n. responsive to the number of OR tokens being less than the maximum number of OR tokens, going to step (i); o. responsive to the number of OR tokens not being less than the maximum number of OR tokens, selecting a next best negative training token from the top N training tokens; p. determining whether a precision improvement is greater than a recall drop; q. responsive to the precision improvement not being greater than the recall drop, going to step (s); r. responsive to the precision improvement being greater than the recall drop, setting the query equal to the query ANDed with the selected next best negative training token; s. determining whether a number of AND NOT tokens is less than a maximum number of AND NOT tokens; t. responsive to the number of AND NOT tokens being less than the maximum number of AND NOT tokens, going to step (o); and u. responsive to the number of AND NOT tokens not being less than the maximum number of AND NOT tokens, going to step (d).