Systems and methods for learning semantic patterns from textual data
First Claim
1. A system comprising at least one processor programmed to:
- process an input text to identify a plurality of semantic patterns that match the input text, wherein, for at least one semantic pattern of the plurality of semantic patterns;
the at least one semantic pattern comprises a valency frame having a plurality of valency frame components;
the plurality of valency frame components correspond, respectively, to a plurality of semantic entities identified from the at least one input text; and
the plurality of semantic entities occur in a common context within the at least one input text; and
use statistical information derived from training data to associate a respective weight with each semantic pattern of the plurality of semantic patterns, wherein, for the at least one semantic pattern, the statistical information comprises at least one measure of mutual information derived from the training data.
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Abstract
In some embodiments, a system is provided comprising at least one processor programmed to process an input text to identify a plurality of semantic patterns that match the input text, wherein, for at least one semantic pattern of the plurality of semantic patterns: the at least one semantic pattern comprises a plurality of semantic entities identified from the at least one input text, and the plurality of semantic entities occur in a common context within the at least one input text. The at least one processor may be further programmed to use statistical information derived from training data to associate a respective weight with each semantic pattern of the plurality of semantic patterns.
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Citations
20 Claims
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1. A system comprising at least one processor programmed to:
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process an input text to identify a plurality of semantic patterns that match the input text, wherein, for at least one semantic pattern of the plurality of semantic patterns; the at least one semantic pattern comprises a valency frame having a plurality of valency frame components; the plurality of valency frame components correspond, respectively, to a plurality of semantic entities identified from the at least one input text; and the plurality of semantic entities occur in a common context within the at least one input text; and use statistical information derived from training data to associate a respective weight with each semantic pattern of the plurality of semantic patterns, wherein, for the at least one semantic pattern, the statistical information comprises at least one measure of mutual information derived from the training data. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method comprising acts of:
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processing an input text to identify a plurality of semantic patterns that match the input text, wherein, for at least one semantic pattern of the plurality of semantic patterns; the at least one semantic pattern comprises a valency frame having a plurality of valency frame components; the plurality of valency frame components correspond, respectively, to a plurality of semantic entities identified from the at least one input text; and the plurality of semantic entities occur in a common context within the at least one input text; and using statistical information derived from training data to associate a respective weight with each semantic pattern of the plurality of semantic patterns, wherein, for the at least one semantic pattern, the statistical information comprises at least one measure of mutual information derived from the training data. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. At least one computer-readable medium having encoded thereon instructions which, when executed by at least one processor, cause the at least one processor to perform a method comprising acts of:
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processing an input text to identify a plurality of semantic patterns that match the input text, wherein, for at least one semantic pattern of the plurality of semantic patterns; the at least one semantic pattern comprises a valency frame having a plurality of valency frame components; the plurality of valency frame components correspond, respectively, to a plurality of semantic entities identified from the at least one input text; and the plurality of semantic entities occur in a common context within the at least one input text; and using statistical information derived from training data to associate a respective weight with each semantic pattern of the plurality of semantic patterns, wherein, for the at least one semantic pattern, the statistical information comprises at least one measure of mutual information derived from the training data. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification