A computer-implemented system and method for generating document training sets is provided. Unclassified documents are provided to two or more classifiers. A classification code assigned to each unclassified document is received. A determination is made as to whether a disagreement exists between classification codes assigned to a common unclassified document via different classifiers. The common unclassified document with a disagreement in classification codes are provided for further review. Results of the further review include one of a new classification code and confirmation of one of the assigned classification codes. The unclassified documents for which a disagreement exists are grouped as a training set.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for generating document training sets, comprising: providing a set of unclassified documents to each of two or more trained classifiers and receiving a classification code assigned to each unclassified document from each classifier; comparing the classification codes assigned to each unclassified document; determining whether a disagreement exists between classification codes assigned to at least one of the unclassified documents via different classifiers; providing the unclassified document with a disagreement in classification codes for further review, wherein results of the further review comprise one of a new classification code and confirmation of one of the assigned classification codes; and grouping the unclassified documents for which a disagreement exists as a training set. 2. A method according to claim 1, further comprising: comparing the classification codes for each of the unclassified documents with the results of the further review; identifying those classification codes that differ from the results of the further review; and re-training the classifiers that assigned the classification codes that differ from the results. 3. A method according to claim 1, further comprising: training the classifiers with coded documents, each coded document assigned with a classification code. 4. A method according to claim 1, further comprising: identifying features within one or more coded documents; classifying the features; and utilizing the classified features as a training set for training the classifiers. 5. A method according to claim 4, wherein the feature classification is performed by an individual. 6. A method according to claim 4, further comprising: identifying via the classifiers one or more features within at least one of the unclassified documents; assigning by each of the classifiers, a classification code to each of the identified features; comparing the classification codes assigned to each feature; determining whether a disagreement exists between the classification codes assigned to at least one of the features via different classifiers; providing the features with a disagreement in classification codes for further review, wherein results of the further review comprise one of a new classification code and confirmation of one of the assigned classification codes; and grouping as a further training set the unclassified documents associated with the features for which a disagreement exists. 7. A method according to claim 6, further comprising: combining the training set and the further training set as a combined training set. 8. A method according to claim 7, further comprising: providing the combined training set to one of an individual for manual classification and to one or more classifiers. 9. A method according to claim 6, further comprising: highlighting areas of the unclassified documents that include the features in disagreement; and identifying via the highlighted areas, areas of the unclassified documents that are interesting 10. A method according to claim 1, wherein the further review is performed by an individual. 11. A computer-implemented system for generating document training sets, comprising: a set of unclassified documents provided to each of two or more trained classifiers, wherein a classification code assigned to each unclassified document from each classifier; a comparison module to compare the classification codes assigned to each unclassified document; a determination module to determine whether a disagreement exists between classification codes assigned to at least one of the unclassified documents via different classifiers; a review module to provide the unclassified document with a disagreement in classification codes for further review, wherein results of the further review comprise one of a new classification code and confirmation of one of the assigned classification codes; a training module to group the unclassified documents for which a disagreement exists as a training set; and a processor to execute the modules. 12. A system according to claim 11, further comprising: a further comparison module to compare the classification codes for each of the unclassified documents with the results of the further review; an identification module to identify those classification codes that differ from the results of the further review; and a re-training module to re-train the classifiers that assigned the classification codes that differ from the results. 13. A system according to claim 11, further comprising: a training module to train the classifiers with coded documents, each coded document assigned with a classification code. 14. A system according to claim 11, further comprising: a feature identification module to identify features within one or more coded documents; a classification module to classify the features; and a training module to utilize the classified features as a training set for training the classifiers. 15. A system according to claim 14, wherein the feature classification is performed by an individual. 16. A system according to claim 14, further comprising: an identification module to identify via the classifiers one or more features within at least one of the unclassified documents; an assignment module to assign by each of the classifiers, a classification code to each of the identified features; a feature comparison module to compare the classification codes assigned to each feature; a feature determination module to determine whether a disagreement exists between the classification codes assigned to at least one of the features via different classifiers; a feature review module to provide the features with a disagreement in classification codes for further review, wherein results of the further review comprise one of a new classification code and confirmation of one of the assigned classification codes; and a feature training module to group as a further training set the unclassified documents associated with the features for which a disagreement exists. 17. A system according to claim 16, further comprising: a combination module to combine the training set and the further training set as a combined training set. 18. A system according to claim 17, further comprising: a delivery module to provide the combined training set to one of an individual for manual classification and to one or more classifiers. 19. A system according to claim 16, further comprising: an identification module to highlight areas of the unclassified documents that include the features in disagreement and to identify via the highlighted areas, areas of the unclassified documents that are interesting. 20. A system according to claim 11, wherein the further review is performed by an individual.