A method, system and computer-usable medium are disclosed for using a context dependency graph to automate the generation of an incorrect answer to a question suitable for a multiple choice exam. A reference corpus is used to generate a concept dependency graph that contains reference keywords and concepts associated with the subject domain of an input corpus. Relationships between the reference keywords and concepts within the concept dependency graph are identified. Once identified, they are used to process a set of input keywords and concepts extracted from the input corpus, and the reference keywords and concepts, to generate a set of distractor words. The resulting set of distractor words is then processed with a set of QA pairs associated with the input corpus to generate a set of multiple choice question-answers that include various distractor answers.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for automating the generation of an incorrect answer to a question suitable for a multiple choice exam, comprising: receiving an input corpus associated with a subject domain; using a reference corpus to generate a concept dependency graph containing reference keywords and concepts associated with the subject domain; identifying relationships between reference keywords and concepts within the concept dependency graph; and using the identified relationships to process a set of input keywords and concepts, and the reference keywords and concepts, to generate a set of distractor words. 2. The method of claim 1, further comprising: processing the set of distractor words with a set of question-answer (QA) pairs associated with the input corpus to generate a set of multiple choice question-answers containing a set of distractor answers. 3. The method of claim 2, wherein: the set of input keywords and concepts are extracted from the input corpus and the set of associated question-answer (QA) pairs. 4. The method of claim 1, wherein: the distractor words are located in the concept dependency graph and not in the input corpus. 5. The method of claim 2, further comprising: eliminating individual multiple choice question-answers in the set of multiple choice question-answers according to a question quality criteria based upon aspects selected from a group consisting of specificity, context, and ambiguity. 6. The method of claim 5, wherein: a machine learning model and a natural language processing (NLP) library are used to determine the individual multiple choice question-answers to be eliminated. 7. A system comprising: a processor; a data bus coupled to the processor; and a computer-usable medium embodying computer program code, the computer-usable medium being coupled to the data bus, the computer program code used for automating the generation of an incorrect answer to a question suitable for a multiple choice exam and comprising instructions executable by the processor and configured for: receiving an input corpus associated with a subject domain; using a reference corpus to generate a concept dependency graph containing reference keywords and concepts associated with the subject domain; identifying relationships between reference keywords and concepts within the concept dependency graph; and using the identified relationships to process a set of input keywords and concepts, and the reference keywords and concepts, to generate a set of distractor words. 8. The system of claim 7, further comprising: processing the set of distractor words with a set of question-answer (QA) pairs associated with the input corpus to generate a set of multiple choice question-answers containing a set of distractor answers. 9. The system of claim 8, wherein: the set of input keywords and concepts are extracted from the input corpus and the set of associated question-answer (QA) pairs. 10. The system of claim 7, wherein: the distractor words are located in the concept dependency graph and not in the input corpus. 11. The system of claim 8, further comprising: eliminating individual multiple choice question-answers in the set of multiple choice question-answers according to a question quality criteria based upon aspects selected from a group consisting of specificity, context, and ambiguity. 12. The system of claim 11, wherein: a machine learning model and a natural language processing (NLP) library are used to determine the individual multiple choice question-answers to be eliminated. 13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for: receiving an input corpus associated with a subject domain; using a reference corpus to generate a concept dependency graph containing reference keywords and concepts associated with the subject domain; identifying relationships between reference keywords and concepts within the concept dependency graph; and using the identified relationships to process a set of input keywords and concepts, and the reference keywords and concepts, to generate a set of distractor words. 14. The non-transitory, computer-readable storage medium of claim 13, further comprising: processing the set of distractor words with a set of question-answer (QA) pairs associated with the input corpus to generate a set of multiple choice question-answers containing a set of distractor answers. 15. The non-transitory, computer-readable storage medium of claim 14, wherein: the set of input keywords and concepts are extracted from the input corpus and the set of associated question-answer (QA) pairs. 16. The non-transitory, computer-readable storage medium of claim 13, wherein: the distractor words are located in the concept dependency graph and not in the input corpus. 17. The non-transitory, computer-readable storage medium of claim 14, further comprising: eliminating individual multiple choice question-answers in the set of multiple choice question-answers according to a question quality criteria based upon aspects selected from a group consisting of specificity, context, and ambiguity. 18. The non-transitory, computer-readable storage medium of claim 17, wherein: a machine learning model and a natural language processing (NLP) library are used to determine the individual multiple choice question-answers to be eliminated. 19. The non-transitory, computer-readable storage medium of claim 13, wherein the computer executable instructions are deployable to a client system from a server system at a remote location. 20. The non-transitory, computer-readable storage medium of claim 13, wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis.