1. A method for automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature, the method comprising: performing, by a computer system, deep semantic analysis of a digital work of literature to create annotations for one or more literary elements, wherein the deep semantic analysis comprises a combination of information retrieval operations, natural language processing, knowledge representation, and machine learning; assigning, by a computer system, an importance and relevance weight to each of the annotations according as determined by the deep semantic analysis; identifying, by a computer system, from the deep semantic analysis, one or more plot devices within each literary plot in the digital work of literature, wherein the one or more plot devices are distinguished from general theme and general plot, associating, by a computer system, the one or more plot devices with one or more non-plot device literary elements; identifying, by a computer system, a theme for each of the one or more plot devices; combining, by a computer system, the importance and relevance weights associated with each respective interrelationship to yield an overall weight for each of the interrelationships; and producing, by a computer system, an output depiction on a user interface device, the annotations, the interrelationships, and the overall weights, for facilitating the user to easily and conveniently see an overall make-up of the work of literature, to be informed of where each literary element exists in that make-up, and to understand relative strengths of each literary element within the context of the make-up.
1. A method for automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature, the method comprising: performing, by a computer system, deep semantic analysis of a digital work of literature to create annotations for one or more literary elements, wherein the deep semantic analysis comprises a combination of information retrieval operations, natural language processing, knowledge representation, and machine learning; assigning, by a computer system, an importance and relevance weight to each of the annotations according as determined by the deep semantic analysis; identifying, by a computer system, from the deep semantic analysis, one or more plot devices within each literary plot in the digital work of literature, wherein the one or more plot devices are distinguished from general theme and general plot, associating, by a computer system, the one or more plot devices with one or more non-plot device literary elements; identifying, by a computer system, a theme for each of the one or more plot devices; combining, by a computer system, the importance and relevance weights associated with each respective interrelationship to yield an overall weight for each of the interrelationships; and producing, by a computer system, an output depiction on a user interface device, the annotations, the interrelationships, and the overall weights, for facilitating the user to easily and conveniently see an overall make-up of the work of literature, to be informed of where each literary element exists in that make-up, and to understand relative strengths of each literary element within the context of the make-up. 5. The method as set forth in claim 1 wherein at least one literary element weight is normalized across a segment of the digital work of literature in which the literary element is found.