UTILIZING USER-VERIFIED DATA FOR TRAINING CONFIDENCE LEVEL MODELS
First Claim
1. A method, comprising:
- performing, by a processing device, syntactico-semantic analysis of a natural language text to produce a plurality of semantic structures;
interpreting, using a set of production rules, the plurality of semantic structures to extract a plurality of information objects representing entities referenced by the natural language text;
determining an attribute value for an information object of the plurality of information objects;
determining a confidence level associated with the attribute value, by evaluating a confidence function associated with the set of production rules;
responsive to determining that the confidence level falls below a threshold confidence value, verifying the attribute value;
appending, to a training data set, at least part of the natural language text referencing the information object and the attribute value; and
determining, using the training data set, at least one parameter of the confidence function.
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Accused Products
Abstract
Systems and methods for utilizing user-verified data for training confidence level models. An example method comprises: performing syntactico-semantic analysis of a natural language text to produce a plurality of semantic structures; interpreting, using a set of production rules, the plurality of semantic structures to extract a plurality of information objects representing entities referenced by the natural language text; determining an attribute value for an information object of the plurality of information objects; determining a confidence level associated with the attribute value, by evaluating a confidence function associated with the set of production rules; responsive to determining that the confidence level falls below a threshold confidence value, verifying the attribute value; appending, to a training data set, at least part of the natural language text referencing the information object and the attribute value; and determining, using the training data set, at least one parameter of the confidence function.
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Citations
20 Claims
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1. A method, comprising:
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performing, by a processing device, syntactico-semantic analysis of a natural language text to produce a plurality of semantic structures; interpreting, using a set of production rules, the plurality of semantic structures to extract a plurality of information objects representing entities referenced by the natural language text; determining an attribute value for an information object of the plurality of information objects; determining a confidence level associated with the attribute value, by evaluating a confidence function associated with the set of production rules; responsive to determining that the confidence level falls below a threshold confidence value, verifying the attribute value; appending, to a training data set, at least part of the natural language text referencing the information object and the attribute value; and determining, using the training data set, at least one parameter of the confidence function. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system, comprising:
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a memory; a processor, coupled to the memory, the processor configured to; perform syntactico-semantic analysis of the natural language text to produce a plurality of semantic structures; interpret, using a set of production rules, the plurality of semantic structures to extract a plurality of information objects representing entities referenced by the natural language text; determine an attribute value for an information object of the plurality of information objects; determine a confidence level associated with the attribute value, by evaluating a confidence function associated with the set of production rules; responsive to determining that the confidence level falls below a threshold confidence value, verify the attribute value; append, to a training data set, at least part of the natural language text referencing the information object and the attribute value; and determine, using the training data set, at least one parameter of the confidence function. - View Dependent Claims (11, 12, 13, 14, 15)
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16. A computer-readable non-transitory storage medium comprising executable instructions that, when executed by a computer system, cause the computer system to:
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perform syntactico-semantic analysis of the natural language text to produce a plurality of semantic structures; interpret, using a set of production rules, the plurality of semantic structures to extract a plurality of information objects representing entities referenced by the natural language text; determine an attribute value for an information object of the plurality of information objects; determine a confidence level associated with the attribute value, by evaluating a confidence function associated with the set of production rules; responsive to determining that the confidence level falls below a threshold confidence value, verify the attribute value; append, to a training data set, at least part of the natural language text referencing the information object and the attribute value; and determine, using the training data set, at least one parameter of the confidence function. - View Dependent Claims (17, 18, 19, 20)
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Specification