Automatic Semantic Rating and Abstraction of Literature

摘要

Deep semantic analysis is performed on an electronic literary work in order to detect plot elements and optional other storyline elements such as characters within the work. Multiple levels of abstract are generated into a model representing the literary work, wherein each element in each abstraction level may be independently rated for preference by a user. Through comparison of multiple abstraction models and one or more user rating preferences, one or more alternative literary works may be automatically recommended to the user.

主权项

1. A method for automatic semantic rating and abstraction of literature comprising:
extracting by computer system a series of annotations from a first electronic text; identifying by a computer system a deep semantic relationship for entries in the annotations to identify literary elements; abstracting iteratively by a computer system from the literary elements to form two or more levels in a first electronic model representing the electronic text, wherein the literary elements and deep semantic relationships between the elements are represented in a hierarchical manner; comparing by a computer system one or more literary elements and the deep semantic relationships in the first electronic model to one or more literary elements of a second electronic model representing a second electronic text, wherein the second electronic text has an associated user preference indicator; responsive to the comparing finding a match above a pre-determined degree of similarity, declaring by a computer system the first electronic text to have an expected user preference indicator as the user preference indicator associated with the second electronic text; and responsive to the comparing finding a match above pre-determined degree of dissimilarity, declaring by a computer system the first electronic text to have an expected user preference indicator complementary the user preference indicator associated with the second electronic text.