A mechanism is provided in a computing device configured with instructions executing on a processor of the computing device to implement a question answering system, for answer scoring based on a combined informativity and specificity score. The question answering system, executing on the processor of the computing device and configured with a question answering machine learning model, generates a set of candidate answers for a user-generated input question. For each given candidate answer in the set of candidate answers, an informativity and specificity scorer of the question answering system determines a specificity value of each term in the given candidate answer based on a position of the term in a taxonomy data structure and determining a specificity score of the given candidate answer based on the specificity value of the terms in the given candidate answer. For each given candidate answer in the set of candidate answers, the informativity and specificity scorer determines an informativity value of each term in the given candidate answer using corpus statistics and determining an informativity score of the given candidate answer based on the informativity value of the terms in the given candidate answer. The question answering system determines a confidence score for each candidate answer within the set of candidate answers based on its specificity value and informativity value. The question answering system ranks the set of candidate answers according to confidence score to form a ranked set of candidate answers. The question answering system returns the ranked set of candidate answers.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a computing device configured with instructions executing on a processor of the computing device to implement a question answering system, for answer scoring based on a combined informativity and specificity score, the method comprising: generating, by the question answering system executing on the processor of the computing device and configured with a question answering machine learning model, a set of candidate answers for a user-generated input question; for each given candidate answer in the set of candidate answers, determining, by an informativity and specificity scorer of the question answering system, a specificity value of each term in the given candidate answer based on a position of the term in a taxonomy data structure and determining a specificity score of the given candidate answer based on the specificity value of the terms in the given candidate answer; for each given candidate answer in the set of candidate answers, determining, by the informativity and specificity scorer, an informativity value of each term in the given candidate answer using corpus statistics and determining an informativity score of the given candidate answer based on the informativity value of the terms in the given candidate answer; determining, by the question answering system, a confidence score for each candidate answer within the set of candidate answers based on its specificity value and informativity value; ranking, by the question answering system, the set of candidate answers according to confidence score to form a ranked set of candidate answers; and returning, by the question answering system, the ranked set of candidate answers. 2. The method of claim 1, wherein each node of the taxonomy data structure is assigned a specificity value. 3. The method of claim 2, wherein each node of the taxonomy data structure has an associated informativity value; and wherein determining the specificity value of each term in the given candidate answer comprises, responsive to the specificity scorer determining that a given term in the given candidate answer does not occur in the taxonomy data structure; aligning the given term with a node in the taxonomy data structure based on informativity value; and assigning specificity value of the node in the taxonomy data structure to be the specificity value of the given term. 4. The method of claim 3, wherein an informativity value of a given taxonomic group within the taxonomy data structure is an average of informativity values of member nodes in the taxonomic group. 5. The method of claim 2, wherein specificity values of the taxonomy data structure are determined heuristically or using a machine learning approach. 6. The method of claim 1, wherein determining the informativity value for the given term comprises determining an inverse Zipfian ranking of the given term as the informativity value. 7. The method of claim 1, wherein determining the specificity score of the given candidate answer comprises determining a highest specificity value of the terms in the given candidate answer to be the specificity score of the given candidate answer. 8. The method of claim 1, further comprising determining, by the informativity and specificity scorer, a combined informativity and specificity score for each given candidate answer based on the specificity value and the informativity value of the given candidate answer. 9. The method of claim 8, wherein determining the combined informativity and specificity score comprises multiplying the specificity value and the informativity value to form the combined informativity and specificity score. 10. The method of claim 1, wherein determining the confidence score for each candidate answer comprises providing the specificity value and the informativity value as inputs to a machine learning model. 11. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program comprises instructions, which when executed on a processor of a computing device causes the computing device to implement a question answering system for answer scoring based on a combined informativity and specificity score, wherein the computer readable program causes the computing device to: generate, by the question answering system executing on the processor of the computing device and configured with a question answering machine learning model, a set of candidate answers for a user-generated input question; for each given candidate answer in the set of candidate answers, determine, by an informativity and specificity scorer of the question answering system, a specificity value of each term in the given candidate answer based on a position of the term in a taxonomy data structure and determine a specificity score of the given candidate answer based on the specificity value of the terms in the given candidate answer; for each given candidate answer in the set of candidate answers, determine, by the informativity and specificity scorer, an informativity value of each term in the given candidate answer using corpus statistics and determine an informativity score of the given candidate answer based on the informativity value of the terms in the given candidate answer; determine, by the question answering system, a confidence score for each candidate answer within the set of candidate answers based on its specificity value and informativity value; rank, by the question answering system, the set of candidate answers according to confidence score to form a ranked set of candidate answers; and return, by the question answering system, the ranked set of candidate answers. 12. The computer program product of claim 11, wherein each node of the taxonomy data structure is assigned a specificity value. 13. The computer program product of claim 12, wherein each node of the taxonomy data structure has an associated informativity value; and wherein determining the specificity value of each term in the given candidate answer comprises, responsive to the specificity scorer determining that a given term in the given candidate answer does not occur in the taxonomy data structure; aligning the given term with a node in the taxonomy data structure based on informativity value; and assigning specificity value of the node in the taxonomy data structure to be the specificity value of the given term. 14. The computer program product of claim 13, wherein an informativity value of a given taxonomic group within the taxonomy data structure is an average of informativity values of member nodes in the taxonomic group. 15. The computer program product of claim 12, wherein specificity values of the taxonomy data structure are determined heuristically or using a machine learning approach. 16. The computer program product of claim 11, wherein determining the informativity value for the given term comprises determining an inverse Zipfian ranking of the given term as the informativity value. 17. The computer program product of claim 11, wherein determining the specificity score of the given candidate answer comprises determining a highest specificity value of the terms in the given candidate answer to be the specificity score of the given candidate answer. 18. The computer program product of claim 11, further comprising determining, by the informativity and specificity scorer, a combined informativity and specificity score for each given candidate answer based on the specificity value and the informativity value of the given candidate answer. 19. The computer program product of claim 18, wherein determining the combined informativity and specificity score comprises multiplying the specificity value and the informativity value to form the combined informativity and specificity score. 20. A computing device comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions, which when executed on a processor of a computing device causes the computing device to implement a question answering system for answer scoring based on a combined informativity and specificity score, wherein the instructions cause the processor to: generate, by the question answering system executing on the processor of the computing device and configured with a question answering machine learning model, a set of candidate answers for a user-generated input question; for each given candidate answer in the set of candidate answers, determine, by an informativity and specificity scorer of the question answering system, a specificity value of each term in the given candidate answer based on a position of the term in a taxonomy data structure and determine a specificity score of the given candidate answer based on the specificity value of the terms in the given candidate answer; for each given candidate answer in the set of candidate answers, determine, by the informativity and specificity scorer, an informativity value of each term in the given candidate answer using corpus statistics and determine an informativity score of the given candidate answer based on the informativity value of the terms in the given candidate answer; determine, by the question answering system, a confidence score for each candidate answer within the set of candidate answers based on its specificity value and informativity value; rank, by the question answering system, the set of candidate answers according to confidence score to form a ranked set of candidate answers; and return, by the question answering system, the ranked set of candidate answers.