Interactive validation of annotated documents

1. A method, comprising:

receiving, by a computer device, an electronic document having labels;

predicting, by the computer device, a user will reject the labels;

determining, by the computer device and in response to the predicting the user will reject the labels, that a subset of labels of the labels violate association rules;

marking, by the computer device, the subset of labels which violate the association rules for validation;

prioritizing, by the computer device, the subset of labels which violate the association rules; and

rendering, by the computer device, the subset of labels which violate the association rules in view of priority, wherein the priority includes an importance of a label in the subset of labels, and

the labels are multi-class labels which include a functional requirement, a non-functional requirement, a verb, and a classification,

the functional requirement represents a description of how a feature functions,

the non-functional requirement represents properties and constraints for the feature,

the verb includes a phrase which describes an action, and

the classification represents a category.

2. The method of claim 1 , wherein the predicting the user will reject the labels includes applying a decision tree to the labels.

3. The method of claim 2 , wherein the decision tree includes branches which represent attributes for the labels.

4. The method of claim 1 , further comprising mining, by the computer device, the association rules from historical data of electronic documents.

5. The method of claim 1 , wherein the marking the subset of labels includes marking for multiple user validation.

6. The method of claim 1 , further comprising prompting, by the computer device, a user to resolve a conflict between the association rules.

7. The method of claim 1 , wherein the rendering the subset of labels includes zooming into the subset of labels.

8. The method of claim 1 , wherein the rendering the subset of labels includes changing a color of font within the subset of labels.

9. The method of claim 1 , wherein the rendering the subset of labels includes changing a font size of font within the subset of labels.

10. The method of claim 1 , wherein the rendering the subset of labels includes enlarging text within the subset of labels.

11. The method of claim 1 , wherein the labels include annotations representing corrections to the labels.

12. The method of claim 1 , wherein the prioritizing the subset of labels includes prioritizing the subset of labels in view of a label confidence, a label frequency, a phrase frequency, resolution of conflicts between the association rules, and user expertise.

13. A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:

learn label attributes from user validated documents using machine learning;

receive an electronic document;

annotate labels in the electronic document;

generate a decision tree in view of the label attributes;

apply the decision tree to the labels;

apply association rules to the labels for an amount of validation of the labels;

in response to a label validating an association rule, determine that the label needs a single user validation;

prioritize the labels; and

render the labels in view of priority, wherein the priority includes an importance of a label of the labels,

the labels are multi-class labels which include a functional requirement, a non-functional requirement, a verb, and a classification,

the functional requirement represents a description of how a feature functions,

the non-functional requirement represents properties and constraints for the feature,

the verb includes a phrase which describes an action, and

the classification represents a category.

14. The computer program product of claim 13 , wherein the program instructions are executable to determine whether the labels meet parameters of the decision tree.

15. The computer program product of claim 13 , wherein the rendering the labels includes zooming into rejected labels and enlarging text of the rejected labels.

16. The computer program product of claim 13 , wherein the rendering the labels includes changing a font size of the labels, a color of font of the labels, and styles of the labels.

17. A system comprising:

a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:

learn label attributes from user validated documents using machine learning, wherein the label attributes include a label confidence level, a phrase frequency level within a label, a label frequency level, a label rejection level, and a target attribute;

receive an electronic document;

annotate labels in the electronic document;

generate a decision tree in view of the label attributes;

apply the decision tree to the labels;

apply association rules to the labels;

mark a subset of the labels which violate the association rules for multiple user validation;

prioritize the subset of labels; and

change text within the subset of labels by changing at least one of a font size of the text, a color of the text, or styles of the text, wherein:

the labels are multi-class labels which include a functional requirement, a nonfunctional requirement, a verb, and a classification,

the functional requirement represents a description of how a feature functions,

the non-functional requirement represents properties and constraints for the feature,

the verb includes a phrase which describes an action, and

the classification represents a category.

18. The system of claim 17 , wherein the program instructions are executable to determine association rules from historical data of electronic documents.

19. The system of claim 17 , wherein the program instructions are executable to prompt a user to resolve a conflict between the association rules.

20. The system of claim 17 , wherein the prioritizing the subset of labels includes prioritizing the subset of labels in view of a label confidence, a label frequency, a phrase frequency, resolution of conflicts between the association rules, and user expertise.