A computer implemented method predicting a next event in a sequence of events in a story narrative. The method reads narrative data, the narrative data comprising a sequence of words arranged in sentence patterns. The method extracts event records from the narrative data, the event records ordered to create a story. Mapping event records to a story rule is then performed to create a sequence of story events based on the event records and the story rule. The technology then outputs a first predictive event based on the sequence of story events, the predictive story event generated from at least one predictive method of a plurality of predictive methods. The outputting is repeated for a second predictive method of the plurality of predictive methods to produce a second predictive event.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method predicting a next event in a sequence of events in a story narrative, comprising: reading narrative data, the narrative data comprising a sequence of words arranged in sentence patterns; extracting event records from the narrative data, the event records ordered to create a story; mapping event records to a story rule to create a sequence of story events based on the event records and the story rule; outputting a first predictive story event based on the sequence of story events, the first predictive story event generated from at least one predictive method of a plurality of predictive methods; and repeating the outputting for a second predictive method of the plurality of predictive methods to produce at least a second predictive story event. 2. The computer implemented method of claim 1 wherein one of the plurality of predictive methods comprises a statistical prediction of a next event in a chain of events. 3. The computer implemented method of claim 1 wherein the narrative data includes at least one protagonist, and one of the plurality of predictive methods comprises evaluating semiotic similarities in behavior. 4. The computer implemented method of claim 1 wherein the narrative data includes at least one protagonist, and wherein one of the plurality of predictive methods includes evaluating narrative balance per protagonist. 5. The computer implemented method of claim 1 wherein the method further includes assigning a first predictive weight to the first predictive story event and a second predictive weight to the second predictive story event, and the method further includes outputting one of the first predictive story event and the second predictive story event based on a comparison of the first predictive weight and the second predictive weight. 6. The computer implemented method of claim 1 wherein the method further includes outputting both of the first predictive story event and the second predictive event. 7. The computer implemented method of claim 1 further including selecting ones of event records in the story and swapping root terms of the event records selected based on a semiotic square of a root term in the event record, and repeating the outputting for at least a third predictive story event based on said swapping. 8. A machine implemented method, comprising; accessing narrative data, the narrative data arranged in sentence patterns; reading a classification data structure, the data classifies at least one verb in each sentence pattern as a functional type, at least one functional type characterizing a symmetrical relationship between a first actor and a second actor in the narrative data; parsing each sentence pattern of narrative data which includes a verb matching a functional type to match sentence subjects and objects to an event template; storing data from each subject and object as an event record; mapping event records to a story rule to create a sequence of story events based on the event records and the story rule; and generating a first predictive story event based on the sequence of story events, the first predictive story event generated from a first predictive method, the first predictive story event comprising an event subsequent to the sequence of story events, the first predictive event method selected from a plurality of predictive methods; wherein the narrative data includes at least one protagonist, and the first predictive method comprises evaluating semiotic similarities in behavior; and outputting the first predictive event to a user perceptible output. 9. The machine implemented method of claim 8 wherein a first predictive method comprises a statistical prediction of a next event in a chain of events. 10. The machine implemented method of claim 8 wherein the method comprises evaluating semiotic similarities in behavior of the at least one protagonist to another protagonist in one or more sequences of story events. 11. The machine implemented method of claim 8 wherein the narrative data includes at least one protagonist, and wherein the first predictive method includes evaluating narrative balance per protagonist within the sequence of story events. 12. The machine implemented method of claim 8 wherein the method further includes assigning a predictive weight to the first event and the second event, and the method further includes outputting one of the first and the second event based on a comparison of the first predictive weight and the second predictive weight. 13. The machine implemented method of claim 8 wherein the method further includes outputting both of the first and the second predictive events. 14. The machine implemented method of claim 8 further including swapping elements of the event records based on a semiotic square of a root term in an element of the record and repeating the generating a first predictive event and a second predictive event. 15. A computing system, comprising: a processor and a computer storage device; a data structure including a classification table, the classification table includes a plurality of verbs classified as functional types, at least one functional type characterizes a contractual relationship between a first actor and a second actor in the narrative data, the classification table includes semiotic square data for each of the plurality of verbs; code in the storage device configured to parse each sentence of sentence data for a verb matching a functional type to match sentence subjects and objects to an event template; code in the storage device configured to create an event record from elements in the sentence data, the code reading the data structure to create the event record based on the functional type; code in the storage device configured to create a sequence of story events based on the event records and the story rule; and code in the storage device configured to generate a first predictive event based on the sequence of story events, the predictive story event generated from at least one predictive method; code in the storage device configured to generate a second predictive event based on the sequence of story events, the predictive story event generated from a second predictive method; code in the storage device configured to output at least one of the first predictive event and the second predictive event. 16. The computing system of claim 15 wherein the code in the storage device configured to generate a first predictive event calculates a statistical prediction of a next event in a chain of events. 17. The computing system of claim 15 wherein the code in the storage device configured to generate a first predictive event calculates narrative semiotic similarities in behavior of at least one protagonist in the sequence of story events to another protagonist in one or more additional sequences of story events. 18. The computing system of claim 15 wherein the code in the storage device configured to generate a first predictive event calculates narrative balance per protagonist within the sequence of story events. 19. The computing system of claim 15 wherein the code in the storage device configured to output at least one of the first predictive event and the second predictive event calculates a predictive weight for the first event and the second event, and the code outputs one of the first and the second event based on a comparison of the first predictive weight and the second predictive weight. 20. The computing system of claim 15 further including code in the storage device configured to generate at least a third predictive event, the code selects ones of event records in the story and swaps root terms of the event records selected based on a semiotic square of each root term in the event record, the code outputs at least a third predictive event based on said swap.