A system and method for automatically mapping LATs and candidate answers to multiple taxonomies without a need to merge these taxonomies. The method includes using a syntactic analysis of a corpus to extract all type instances of the LAT. The extracted instances are then mapped to a given taxonomy and clustered in a set of supertypes. Each supertype receives a score based on the coverage of LAT instances in the corpus. The method includes mapping the candidate answer to the same taxonomy to determine if the candidate answer is an instance of a significant supertype. Then the score of a candidate answer is obtained by aggregating or taking a maximum of the score of the matched significant supertypes. This score evaluates the type match between the LAT and candidate answer for a taxonomy. Multiple taxonomies can be used to increase the chance of LAT and candidate answer mapping.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for scoring candidate answers to questions, said method comprising: receiving a natural language question into a computer-implemented question answering system; applying syntactic analysis on said question to determine a lexical answer type (LAT) for said question; generating a second question based on said LAT; identifying one or more LAT instances by inputting said generated second question through a search engine; mapping said one or more LAT instances to one or more taxonomies; identifying, in said one or more taxonomies, one or more supertype entities associated with said mapped LAT instances; generating a type evaluation score for a candidate answer based on a match between said candidate answer and a supertype entity within a taxonomy; generating one or more answers to said question, wherein each answer has an associated confidence score based at least in part on said type evaluation score. 2. The computer-implemented method as claimed in claim 1, wherein identifying one or more LAT instances is carried out by said search engine obtaining one or more answers to said second question from a corpus and assigning at least a portion of said answers as said one or more LAT instances. 3. The computer-implemented method as claimed in claim 2, wherein said corpus is a domain-specific knowledge base. 4. The computer-implemented method as claimed in claim 2, wherein said question answering system is a probabilistic system that obtains answers from a knowledge base comprising unstructured text. 5. The computer-implemented method as claimed in claim 1, wherein said supertype entities are significant supertype entities. 6. The computer-implemented method as claimed in claim 5, wherein said significant supertype entities are determined by calculating a recall loss between a parent node and child node within a taxonomy, and the parent node having the smallest recall loss exceeding a threshold is identified as a significant supertype entity. 7. The computer-implemented method as claimed in claim 6, wherein a recall loss indicates the difference in the percentage of LAT instances covered by that LAT node compared to its child node in the taxonomy. 8. The computer-implemented method as claimed in claim 1, wherein said type evaluation score for a candidate answer matched to a supertype entity comprises a coverage value for said supertype entity indicating a percentage of LAT instances covered by said supertype entity. 9. The computer-implemented method as claimed in claim 8, wherein a type evaluation score is generated for each taxonomy. 10. The computer-implemented method as claimed in claim 9, wherein the step of generating said type evaluation score comprises taking a maximum of said type evaluation scores for multiple taxonomies. 11. A system for scoring candidate answers to questions comprising: a memory storage device; a hardware processor, coupled to said memory storage device, for receiving instructions therefrom to configure said hardware processor to perform a method comprising: receiving a natural language question; applying syntactic analysis on said question to determine a lexical answer type (LAT) for said question; generating a second question based on said LAT; identifying one or more LAT instances by obtaining answers to said generated second question from a knowledge base; mapping said one or more LAT instances to one or more taxonomies; identifying, in said one or more taxonomies, one or more supertype entities associated with said mapped LAT instances; generating a type evaluation score for a candidate answer based on a match between said candidate answer and a supertype entity within a taxonomy; generating one or more answers to said question, wherein each answer has an associated confidence score based at least in part on said type evaluation score. 12. The system as claimed in claim 11, further comprising a storage device storing a corpus of electronic documents serving as a knowledge base from which to generate answers. 13. The system as claimed in claim 12, further comprising an electronic display device for displaying said one or more answers. 14. The system as claimed in claim 12, wherein said obtained answers are the one or more LAT instances. 15. The system as claimed in claim 11, wherein said supertype entities are significant supertype entities. 16. The system as claimed in claim 15, wherein said significant supertype entities are determined by calculating a recall loss between a parent node and child node within a taxonomy, and the parent node having the smallest recall loss exceeding a threshold is identified as a significant supertype entity. 17. The system as claimed in claim 16, wherein said determining if a candidate answer associated with the input query matches a significant supertype entity comprises: 18. A computer program product for scoring candidate answers to questions, said computer program product comprising a computer readable storage medium having program instructions embodied therewith, said program instruction executable by a computer to cause said computer to: receive, from an input device, a natural language question; perform, by a processor, syntactic analysis on said question to determine a lexical answer type (LAT) for said question; generate, by a processor, a second question based on said LAT; identify, by a processor, one or more LAT instances by obtaining answers to said generated second question from a knowledge base; map, by a processor, said one or more LAT instances to one or more taxonomies; identify, by a processor, in said one or more taxonomies, one or more supertype entities associated with said mapped LAT instances; generate, by a processor, a type evaluation score for a candidate answer based on a match between said candidate answer and a supertype entity within a taxonomy; generate, by a processor, one or more answers to said question, wherein each answer has an associated confidence score based at least in part on said type evaluation score. 19. The computer program product of claim 18, wherein said obtained answers are the one or more LAT instances. 20. The computer program product of claim 18, further comprising program instructions to cause the computer to output said one or more answers to an electronic display device.