A method, system and a computer program product are provided for classifying elements in a ground truth training set by iteratively assigning machine-annotated training set elements to clusters which are analyzed to identify a prioritized cluster containing one or more elements which are frequently misclassified and display machine-annotated training set elements associated with the first prioritized cluster along with a warning that the first prioritized cluster contains one or more elements which are frequently misclassified to solicit verification or correction feedback from a human subject matter expert (SME) for inclusion in an accepted training set.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of classifying elements in a ground truth training set, the method comprising: performing, by the information handling system, comprising a processor and a memory, annotation operations on a ground truth training set using an annotator to generate a machine-annotated training set; assigning, by the information handling system, elements from the machine-annotated training set to one or more clusters; analyzing, by the information handling system, the one or more clusters to identify at least a first prioritized cluster containing one or more elements which are frequently misclassified; and displaying, by the information handling system, machine-annotated training set elements associated with the first prioritized cluster along with a warning that the first prioritized cluster contains one or more elements which are frequently misclassified to solicit verification or correction feedback from a human subject matter expert (SME) for inclusion in an accepted training set. 2. The method of claim 1, where the annotator comprises a dictionary annotator, rule-based annotator, or a machine learning annotator. 3. The method of claim 1, where assigning elements from the machine-annotated training set to one or more clusters comprises: generating a vector representation for each element from the machine-annotated training set; and grouping the vector representations for the elements from the machine-annotated training set elements into one or more clusters. 4. The method of claim 1, where analyzing the one or more clusters comprises identifying a group of elements from a confusion matrix that are commonly confused with one another. 5. The method of claim 4, where analyzing the one or more clusters comprises: applying one or more feature selection algorithms to the group of elements from the confusion matrix that are commonly confused with one another to identify error characteristics of each misclassified element; and generating a vector representation for each misclassified element from the error characteristics of each misclassified element. 6. The method of claim 5, where analyzing the one or more clusters comprises detecting an alignment between a vector representation for each misclassified element and a vector representation of the one or more clusters. 7. The method of claim 1, further comprising displaying a reclassification recommendation for a correct classification for at least one of the one or more elements which are frequently misclassified. 8. The method of claim 7, where each reclassification recommendation is paired with a corresponding element which is frequently misclassified based on information derived from a confusion matrix. 9. The method of claim 1, further comprising verifying or correcting classifications for all machine-annotated training set elements in a cluster as a single group based on verification or correction feedback from the human subject matter expert. 10. The method of claim 1, where each element is an entity/relationship element. 11. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on an information handling system, causes the system to classify elements in a ground truth training set by: performing annotation operations on a ground truth training set using an annotator to generate a machine-annotated training set; assigning elements from the machine-annotated training set to one or more clusters; analyzing the one or more clusters to identify at least a first prioritized cluster containing one or more elements which are frequently misclassified; and displaying machine-annotated training set elements associated with the first prioritized cluster along with a warning that the first prioritized cluster contains one or more elements which are frequently misclassified to solicit verification or correction feedback from a human subject matter expert (SME) for inclusion in an accepted training set. 12. The computer program product of claim 10, wherein the computer readable program, when executed on the system, causes the system to assign elements from the machine-annotated training set to one or more clusters by: generating a vector representation for each element from the machine-annotated training set; and grouping the vector representations for the elements from the machine-annotated training set elements into one or more clusters. 13. The computer program product of claim 10, wherein the computer readable program, when executed on the system, causes the system to analyze the one or more clusters by identifying a group of elements from a confusion matrix that are commonly confused with one another. 14. The computer program product of claim 13, wherein the computer readable program, when executed on the system, causes the system to analyze the one or more clusters by: applying one or more feature selection algorithms to the group of elements from the confusion matrix that are commonly confused with one another to identify error characteristics of each misclassified element; and generating a vector representation for each misclassified element from the error characteristics of each misclassified element. 15. The computer program product of claim 14, wherein the computer readable program, when executed on the system, causes the system to analyze the one or more clusters by detecting an alignment between a vector representation for each misclassified element and a vector representation of the one or more clusters. 16. The computer program product of claim 14, wherein the computer readable program, when executed on the system, causes the system to display a reclassification recommendation for a correct classification for at least one of the one or more elements which are frequently misclassified, where each reclassification recommendation is paired with a corresponding element Which is frequently misclassified based on information derived from a confusion matrix. 17. The computer program product of claim 10, further comprising computer readable program, when executed on the system, causes the system to verify or correct classifications for all machine-annotated training set elements in a cluster as a single group based on verification or correction feedback from the human subject matter expert. 18. An information handling system comprising: one or more processors; a memory coupled to at least one of the processors; and a set of instructions stored in the memory and executed by at least one of the processors to classify elements in a ground truth training set, wherein the set of instructions are executable to perform actions of: performing, by the system, annotation operations on a ground truth training set using an annotator to generate a machine-annotated training set; assigning, by the system, elements from the machine-annotated training set to one or more clusters; analyzing, by the system, the one or more clusters to identify at least a first prioritized cluster containing one or more elements which are frequently misclassified; and displaying, by the system, machine-annotated training set elements associated with the first prioritized cluster along with a warning that the first prioritized cluster contains one or more elements which are frequently misclassified to solicit verification or correction feedback from a human subject matter expert (SME) for inclusion in an accepted training set. 19. The information handling system of claim 18, where analyzing the one or more clusters comprises identifying a group of elements from a confusion matrix that are commonly confused with one another. 20. The information handling system of claim 19, where analyzing the one or more clusters comprises: applying one or more feature selection algorithms to the group of elements from the confusion matrix that are commonly confused with one another to identify error characteristics of each misclassified element; and generating a vector representation for each misclassified element from the error characteristics of each misclassified element. 21. The information handling system of claim 20, where analyzing the one or more clusters comprises detecting an alignment between a vector representation for each misclassified element and a vector representation of the one or more clusters. 22. The information handling system of claim 18, further comprising displaying a reclassification recommendation for a correct classification for at least one of the one or more elements which are frequently misclassified, where each reclassification recommendation is paired with a corresponding element which is frequently misclassified based on information derived from a confusion matrix. 23. The information handling system of claim 18, further comprising verifying or correcting all classifications for all machine-annotated training set elements in a cluster as a single group based on verification or correction feedback from the human subject matter expert. 24. The information handling system of claim 18, further comprising verifying or correcting classifications for all machine-annotated training set elements in a cluster one at a time based on verification or correction feedback from the human subject matter expert.