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Systems and methods for learning semantic patterns from textual data

  • US 9,959,341 B2
  • Filed: 06/11/2015
  • Issued: 05/01/2018
  • Est. Priority Date: 06/11/2015
  • Status: Active Grant
First Claim
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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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