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 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 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 hidden meaning translation engine of 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; generating, by the hidden meaning translation engine of data processing system, a hidden meaning statement data structure for the primary portion of natural language content based on the probabilistic model; storing, by the hidden meaning translation engine of the data processing system, the hidden meaning statement data structure in association with the primary portion of natural language content in the one or more corpora of electronic documentation; 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 in the one or more corpora of electronic documentation, 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, and wherein generating a probabilistic model further comprises 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.