UTILIZING FAILURES IN QUESTION AND ANSWER SYSTEM RESPONSES TO ENHANCE THE ACCURACY OF QUESTION AND ANSWER SYSTEMS
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Abstract
A method of enhancing the accuracy of a question-answer system. Missing information from a corpus of data is identified. The missing information is any information that improves a confidence for a candidate answer to a question. A follow-on inquiry is generated. The follow-on inquiry prompts for the missing information to be provided. The follow-on inquiry is output to an external source. A response to the follow-on inquiry is received from the external source. The response is added to the corpus of data.
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Citations
75 Claims
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1-50. -50. (canceled)
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51. A method of enhancing the accuracy of a question-answer system, said method comprising:
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identifying missing information from a corpus of data, wherein said missing information is any information that improves a confidence for a candidate answer to a question; generating a follow-on inquiry, wherein said follow-on inquiry prompts for said missing information to be provided; outputting said follow-on inquiry to an external source; receiving a response to said follow-on inquiry from said external source; and adding said response to said corpus of data. - View Dependent Claims (52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63)
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64. A method of enhancing the accuracy of a question-answer system, said method comprising:
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receiving a question into a computerized question-answer system operating on a computerized device; automatically generating, by said computerized device, a plurality of candidate answers to said question; evaluating, by said computerized device, sources of evidence used to generate said plurality of candidate answers to identify marginal evidence, said marginal evidence contributes only partially to a candidate answer; determining, by said computerized device, a confidence score for each of said plurality of candidate answers; automatically identifying, by said computerized device, information not provided by said marginal evidence that could further develop said confidence score; automatically generating, by said computerized device, at least one follow-on inquiry based on said information; outputting, by said computerized device, said at least one follow-on inquiry to external sources separate from said question-answer system to obtain responses to said at least one follow-on inquiry; receiving, by said computerized device, from said external sources at least one response to said at least one follow-on inquiry; inputting, using said computerized device, said at least one response into said question-answer system; and automatically developing, by said computerized device, additional logical rules and additional evidence for said computerized question-answer system based on said at least one response to said at least one follow-on inquiry. - View Dependent Claims (65, 66, 67, 68)
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69. A method comprising:
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providing a first question to be answered by a Question Answering (QA) system to a processor; creating, by said processor, a collection of candidate answers to said first question, said collection of candidate answers being created from a corpus of data; generating, by said processor, supporting evidence and a confidence score for each said candidate answer; producing, by an evidence analysis module, a second question based on said supporting evidence; presenting, by said processor, said second question to one or more external sources separate from said QA system to obtain responses to said second question; receiving, by said processor, at least one response or knowledge item from said one or more external sources; inputting, by said processor, said at least one response or knowledge item into said corpus of data; and automatically developing additional logical rules and additional evidence for said QA system based on said at least one response or knowledge item. - View Dependent Claims (70, 71)
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72. A method comprising:
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receiving a question into a Question Answering (QA) system; said QA system comparing said question to a corpus of data; said QA system generating hypotheses about relationships between linguistic and semantic entities of said question and said corpus of data; said QA system generating a plurality of candidate answers to said question using said hypotheses; said QA system evaluating sources of evidence used to generate said plurality of candidate answers to identify marginal evidence, said marginal evidence contributes only partially to a candidate answer; said QA system determining a confidence score for each of said plurality of candidate answers; determining a missing piece of common-sense knowledge, said missing piece of common-sense knowledge comprising information that would enable said QA system to further develop said confidence score; formulating a follow-on inquiry to obtain said missing piece of common-sense knowledge; outputting said follow-on inquiry to an external expert community source separate from said QA system to obtain responses to said follow-on inquiry; receiving from said external expert community source responses to said follow-on inquiry comprising said missing piece of common-sense knowledge; inputting said missing piece of common-sense knowledge into said QA system; and said QA system automatically developing additional logical rules and additional evidence for said QA system based on said obtained missing piece of information. - View Dependent Claims (73, 74, 75)
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Specification