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, wherein the memory comprises instructions executed by the processor to cause the processor to be specifically configured to implement a hidden meaning translation engine, the method comprising:
- receiving, by the hidden meaning translation engine of the data processing system, a primary portion of natural language content from one or more corpora of electronic documentation;
identifying, by the hidden meaning translation engine of the data processing system, a secondary portion of natural language content, in the one or more corpora of electronic documentation, that references the primary portion of natural language content;
analyzing, by the hidden meaning translation engine of 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, wherein analyzing the secondary portion of natural language content further comprises correlating a first temporal characteristic of the secondary portion of natural language content with a second temporal characteristic of the primary portion of natural language content;
generating and training, by the hidden meaning translation engine of the data processing system, a probabilistic model based on results of the analysis of the secondary portion of natural language content modeling a probability of hidden meaning in the primary portion of natural language content at least by weighting the secondary portion of natural language content based on whether the first temporal characteristic is at a prior time to the second temporal characteristic or at a later time than the second temporal characteristic;
generating, by the hidden meaning translation engine of the data processing system, a hidden meaning statement data structure for the primary portion of natural language content based on the indications of meaning identified by the analysis of the secondary portion; and
performing, by a cognitive system, a cognitive operation at least by performing natural language processing on a combination of the primary portion of natural language content and the hidden meaning statement data structure.
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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.
46 Citations
17 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, wherein the memory comprises instructions executed by the processor to cause the processor to be specifically configured to implement a hidden meaning translation engine, the method comprising:
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receiving, by the hidden meaning translation engine of the data processing system, a primary portion of natural language content from one or more corpora of electronic documentation; identifying, by the hidden meaning translation engine of the data processing system, a secondary portion of natural language content, in the one or more corpora of electronic documentation, that references the primary portion of natural language content; analyzing, by the hidden meaning translation engine of 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, wherein analyzing the secondary portion of natural language content further comprises correlating a first temporal characteristic of the secondary portion of natural language content with a second temporal characteristic of the primary portion of natural language content; generating and training, by the hidden meaning translation engine of the data processing system, a probabilistic model based on results of the analysis of the secondary portion of natural language content modeling a probability of hidden meaning in the primary portion of natural language content at least by weighting the secondary portion of natural language content based on whether the first temporal characteristic is at a prior time to the second temporal characteristic or at a later time than the second temporal characteristic; generating, by the hidden meaning translation engine of the data processing system, a hidden meaning statement data structure for the primary portion of natural language content based on the indications of meaning identified by the analysis of the secondary portion; and performing, by a cognitive system, a cognitive operation at least by performing natural language processing on a combination of the primary portion of natural language content and the hidden meaning statement data structure. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A computer program product comprising a non-transitory computer readable 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 from one or more corpora of electronic documentation; identify a secondary portion of natural language content, in the one or more corpora of electronic documentation, that references the primary portion of 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, wherein analyzing the secondary portion of natural language content further comprises correlating a first temporal characteristic of the secondary portion of natural language content with a second temporal characteristic of the primary portion of natural language content; generate and train a probabilistic model based on results of the analysis of the secondary portion of natural language content modeling a probability of hidden meaning in the primary portion of natural language content at least by weighting the secondary portion of natural language content based on whether the first temporal characteristic is at a prior time to the second temporal characteristic or at a later time than the second temporal characteristic; generate a hidden meaning statement data structure for the primary portion of natural language content based on the indications of meaning identified by the analysis of the secondary portion; and perform a cognitive operation at least by performing natural language processing on a combination of the primary portion of natural language content and the hidden meaning statement data structure.
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17. 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 from one or more corpora of electronic documentation; identify a secondary portion of natural language content, in the one or more corpora of electronic documentation, that references the primary portion of 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, wherein analyzing the secondary portion of natural language content further comprises correlating a first temporal characteristic of the secondary portion of natural language content with a second temporal characteristic of the primary portion of natural language content; generate and train a probabilistic model based on results of the analysis of the secondary portion of natural language content modeling a probability of hidden meaning in the primary portion of natural language content at least by weighting the secondary portion of natural language content based on whether the first temporal characteristic is at a prior time to the second temporal characteristic or at a later time than the second temporal characteristic; generate a hidden meaning statement data structure for the primary portion of natural language content based on the indications of meaning identified by the analysis of the secondary portion; and perform a cognitive operation at least by performing natural language processing on a combination of the primary portion of natural language content and the hidden meaning statement data structure.
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Specification