17. A system for determining at least one target value of at least one target in at least one document, comprising: at least one processor, wherein the at least one processor is configured for: determining, utilizing at least one scoring application, target position information from at least one training document; using the target position information, utilizing at least one localization module, the using comprising: finding at least one reference; creating at least one reference vector for each reference; performing variance filtering on the at least one reference and the at least one reference vector from each document to obtain any similar references and any similar reference vectors from all documents; using the any similar references and the any similar reference vectors to create at least one dynamic variance network, wherein the at least one dynamic variance network is a set comprising the at least one reference and the at least one reference vector tying each reference of the set to the at least one target; and applying the target position information, utilizing the at least one scoring application, on at least one new document to determine the at least one target value of the at least one target on the at least one new document, wherein the target position information comprises the at least one reference within the training document and the at least one reference vector, each reference vector tying the at least one reference to the at least one target, each reference vector comprising content data indicating at least one intrinsic content type.
17. A system for determining at least one target value of at least one target in at least one document, comprising: at least one processor, wherein the at least one processor is configured for: determining, utilizing at least one scoring application, target position information from at least one training document; using the target position information, utilizing at least one localization module, the using comprising: finding at least one reference; creating at least one reference vector for each reference; performing variance filtering on the at least one reference and the at least one reference vector from each document to obtain any similar references and any similar reference vectors from all documents; using the any similar references and the any similar reference vectors to create at least one dynamic variance network, wherein the at least one dynamic variance network is a set comprising the at least one reference and the at least one reference vector tying each reference of the set to the at least one target; and applying the target position information, utilizing the at least one scoring application, on at least one new document to determine the at least one target value of the at least one target on the at least one new document, wherein the target position information comprises the at least one reference within the training document and the at least one reference vector, each reference vector tying the at least one reference to the at least one target, each reference vector comprising content data indicating at least one intrinsic content type. 28. The system of claim 17 , wherein the at least one reference is: merged with at least one other reference; and/or split into at least two references.