Method to allow for question and answer system to dynamically return different responses based on roles
First Claim
1. A computer implemented method for adjusting answers to questions based on a user criteria by an information handling system capable of answering questions, the system comprising a processor and a memory comprising instructions executed by the processor, the method comprising:
- manually tagging documents for deep natural language processing (NLP);
extracting features from the manually-tagged documents;
providing each of a plurality of documents with an access level based on extracted features;
receiving a question from a user having a user profile comprising a responsibility role of the user and a permission level of the user;
deriving a content access constraint criteria of the user from the responsibility role and the permission level;
generating a plurality of candidate answers for the question;
removing one or more candidate answers from the plurality of candidate answers, wherein the one or more candidate answers have an access level higher than the content access constraint criteria of the user, wherein the access level is provided by performing deep NLP and feature extraction on each text segment of each document, wherein the extracted features include particular entity types, relation types, predicates, and indicating words;
applying natural language processing, deep analytic analysis, and evidence scoring to the plurality of documents having the access level no higher than the content access constraint criteria to select an answer to the question from the remaining candidate answers; and
providing to the user a notification comprising the answer to the question.
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Abstract
Embodiments are directed to a question and answer (QA) pipeline system that adjusts answers to input questions based on a user criteria, thus implementing a content-based determination of access permissions. The QA system allows for information to be retrieved based on permission granted to a user. Documents are ingested and assigned an access level based on a defined information access policy. The QA system is implemented with the defined information access policy, the ingested documents, and the inferred access levels. For the QA system implementation, a user enters a question; primary search and answer extraction stages are performed; candidate answer extraction is performed using only content the user is allowed to access; the candidate answers are scored, ranked, and merged; ranked answers based on user permissions are filtered; and answers are provided to the user.
26 Citations
17 Claims
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1. A computer implemented method for adjusting answers to questions based on a user criteria by an information handling system capable of answering questions, the system comprising a processor and a memory comprising instructions executed by the processor, the method comprising:
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manually tagging documents for deep natural language processing (NLP); extracting features from the manually-tagged documents; providing each of a plurality of documents with an access level based on extracted features; receiving a question from a user having a user profile comprising a responsibility role of the user and a permission level of the user; deriving a content access constraint criteria of the user from the responsibility role and the permission level; generating a plurality of candidate answers for the question; removing one or more candidate answers from the plurality of candidate answers, wherein the one or more candidate answers have an access level higher than the content access constraint criteria of the user, wherein the access level is provided by performing deep NLP and feature extraction on each text segment of each document, wherein the extracted features include particular entity types, relation types, predicates, and indicating words; applying natural language processing, deep analytic analysis, and evidence scoring to the plurality of documents having the access level no higher than the content access constraint criteria to select an answer to the question from the remaining candidate answers; and providing to the user a notification comprising the answer to the question. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer program product for adjusting answers to questions based on a user criteria by an information handling system capable of answering questions, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
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manually tag documents for deep natural language processing (NLP); extract features from the manually-tagged documents; provide each of a plurality of documents with an access level based on extracted features; receive a question from a user having a user profile comprising a responsibility role of the user and a permission level of the user; derive a content access constraint criteria of the user from the responsibility role and the permission level; generate a plurality of candidate answers for the question; remove one or more candidate answers from the plurality of candidate answers, wherein the one or more candidate answers have an access level higher than the content access constraint criteria of the user, wherein the access level is provided by performing deep NLP and feature extraction on each text segment of each document, wherein the extracted features include particular entity types, relation types, predicates, and indicating words; apply natural language processing, deep analytic analysis, and evidence scoring to the plurality of documents having the access level no higher than the content access constraint criteria to select an answer to the question from the remaining candidate answers; and provide to the user a notification comprising the answer to the question. - View Dependent Claims (8, 9, 10, 11)
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12. A system for adjusting answers to questions based on a user criteria by an information handling system capable of answering questions, the system comprising:
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a memory comprising executable instructions; and a processor configured to execute the executable instructions to; manually tag documents for deep natural language processing (NLP); extract features from the manually-tagged documents; provide each of a plurality of documents with an access level based on extracted features; receive a question from a user having a user profile comprising a responsibility role of the user and a permission level of the user; derive a content access constraint criteria of the user from the responsibility role and the permission level; generate a plurality of candidate answers for the question; remove one or more candidate answers from the plurality of candidate answers, wherein the one or more candidate answers have an access level higher than the content access constraint criteria of the user, wherein the access level is provided by performing deep NLP and feature extraction on each text segment of each document, wherein the extracted features include particular entity types, relation types, predicates, and indicating words; apply natural language processing, deep analytic analysis, and evidence scoring to the plurality of documents having the access level no higher than the content access constraint criteria to select an answer to the question from the remaining candidate answers; and provide to the user a notification comprising the answer to the question. - View Dependent Claims (13, 14, 15, 16, 17)
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Specification