Inferring informational goals and preferred level of detail of answers based on application being employed by the user
First Claim
1. A system for inferring an information goal, comprising:
- a query subsystem that receives at least one of a query and an extrinsic data, the query subsystem is operatively coupled to an inference model and a knowledge data store, the query subsystem comprising;
a natural language processor that parses the query; and
an inference engine that infers one or more informational goals based, at least in part, on at least one of the parsed query, the extrinsic data and an inference data stored in the inference model, the inference engine further inferring one or more preferred levels of detail for an answer based on an application being employed by the user.
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Accused Products
Abstract
A system and method for inferring informational goals and preferred level of details in answers in response to questions posed to computer-based information retrieval or question-answering systems is provided. The system includes a query subsystem that can receive an input query and extrinsic data associated with the query and which can output an answer to the query. The query subsystem accesses an inference model to retrieve conditional probabilities that certain informational goals are present. One application of the system includes determining a user'"'"'s likely informational goals and then accessing a knowledge data store to retrieve responsive information. Determining a user'"'"'s likely informational goals can include inferring a desired level of detail of answers to the query based on the application being employed by the user at the time the query is submitted. The system includes a natural language processor that parses queries into observable linguistic features and embedded semantic components that can be employed to retrieve the conditional probabilities from the inference model. The inference model is built by employing supervised learning and statistical analysis on a set of queries suitable to be presented to a question-answering system. Such a set of queries can be manipulated to produce different inference models based on demographic and/or localized linguistic data.
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Citations
20 Claims
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1. A system for inferring an information goal, comprising:
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a query subsystem that receives at least one of a query and an extrinsic data, the query subsystem is operatively coupled to an inference model and a knowledge data store, the query subsystem comprising; a natural language processor that parses the query; and an inference engine that infers one or more informational goals based, at least in part, on at least one of the parsed query, the extrinsic data and an inference data stored in the inference model, the inference engine further inferring one or more preferred levels of detail for an answer based on an application being employed by the user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A computer readable medium storing computer executable components of a system for inferring an information goal, the system comprising:
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a query component that receives a new query and a new extrinsic data, the query component operatively coupled to an inference model and a knowledge data store, the query component comprising; a natural language processing component that parses the new query; and an inference component that infers one or more informational goals based, at least in part, on at least one of, the new query, the new extrinsic data and an inference data stored in the inference model, the inference engine further inferring one or more preferred levels of detail for an answer based on an application being employed by the user.
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20. A computer readable medium storing computer executable components of a system for learning how to infer information goals from queries, the system comprising:
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a natural language processing component that produces a linguistic data concerning one or more linguistic features; a tagging component that manipulates the linguistic data; one or more taggers that manipulates the linguistic data; and an inference model component that stores information concerning conditional probabilities associated with the likelihood that one or more informational goals exist, where the conditional probabilities of the informational goals are determined, at least in part, from Bayesian statistical analysis performed on the linguistic data, the inference engine further inferring one or more preferred levels of detail for an answer based on an application being employed by the user.
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Specification