Extracting Veiled Meaning in Natural Language Content
First Claim
1. A method, in a data processing system comprising a processor and a memory, for identifying hidden meaning in a portion of natural language content, the method comprising:
- receiving, by the data processing system, a primary portion of natural language content;
identifying, by the data processing system, a secondary portion of natural language content that references the natural language content;
analyzing, by the data processing system, the secondary portion of natural language content to identify indications of meaning directed to elements of the primary portion of natural language content;
generating, by the data processing system, a probabilistic model based on the secondary portion of natural language content modeling a probability of hidden meaning in the primary portion of natural language content; and
generating, by the data processing system, a hidden meaning statement data structure for the primary portion of natural language content based on the probabilistic model.
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Abstract
Mechanisms for identifying hidden meaning in a portion of natural language content are provided. A primary portion of natural language content is received and a secondary portion of natural language content is identified that references the natural language content. The secondary portion of natural language content is analyzed to identify indications of meaning directed to elements of the primary portion of natural language content. A probabilistic model is generated based on the secondary portion of natural language content modeling a probability of hidden meaning in the primary portion of natural language content. A hidden meaning statement data structure is generated for the primary portion of natural language content based on the probabilistic model.
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Citations
20 Claims
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1. A method, in a data processing system comprising a processor and a memory, for identifying hidden meaning in a portion of natural language content, the method comprising:
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receiving, by the data processing system, a primary portion of natural language content; identifying, by the data processing system, a secondary portion of natural language content that references the natural language content; analyzing, by the data processing system, the secondary portion of natural language content to identify indications of meaning directed to elements of the primary portion of natural language content; generating, by the data processing system, a probabilistic model based on the secondary portion of natural language content modeling a probability of hidden meaning in the primary portion of natural language content; and generating, by the data processing system, a hidden meaning statement data structure for the primary portion of natural language content based on the probabilistic model. - 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 program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed in a data processing system, causes the data processing system to:
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receive a primary portion of natural language content; identify a secondary portion of natural language content that references the natural language content; analyze the secondary portion of natural language content to identify indications of meaning directed to elements of the primary portion of natural language content; generate a probabilistic model based on the secondary portion of natural language content modeling a probability of hidden meaning in the primary portion of natural language content; and generate a hidden meaning statement data structure for the primary portion of natural language content based on the probabilistic model.
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20. An apparatus comprising:
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a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to; receive a primary portion of natural language content; identify a secondary portion of natural language content that references the natural language content; analyze the secondary portion of natural language content to identify indications of meaning directed to elements of the primary portion of natural language content; generate a probabilistic model based on the secondary portion of natural language content modeling a probability of hidden meaning in the primary portion of natural language content; and generate a hidden meaning statement data structure for the primary portion of natural language content based on the probabilistic model.
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Specification