Detecting Literary Elements in Literature and Their Importance Through Semantic Analysis and Literary Correlation

• US 20150154179A1
• Filed: 09/18/2014
• Published: 06/04/2015
• Est. Priority Date: 12/03/2013
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Allen, Corville Orain, Carrier, Scott Robert, Woods, Eric
• Granted Patent: US 10,073,836 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/169 Annotation, e.g. comment data or footnotes
• CPC Class Codes: G06F 40/30 Semantic analysis

Abstract

Automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature is accomplished by employing natural language processing and deep semantic analysis of text to create annotations for the literary elements found in a segment or in the entirety of the literature, a weight to each literary element and its associated annotations, wherein the weight indicates an importance or relevance of a literary element to at least the segment of the work of literature; correlating and matching the literary elements to each other to establish one or more interrelationships; and producing an overall weight for the correlated matches.

Claims

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 natural language processing and deep semantic analysis of a digital work of literature to create annotations for one or more literary elements;

assigning by a computer system weights to one or more of the annotations according to importance and relevance of each annotation as determined by the natural language processing and the deep semantic analysis;

assigning by a computer system one or more interrelationships and type characteristics to the annotations; and

producing by a computer system an overall weight for one or more of the interrelationships as a digital output.

2. The method as set forth in claim 1 wherein the assigning interrelationships and type characteristics comprises correlating and matching literary elements to each other, and wherein the producing an overall weight comprises assigning overall weights to one or more correlated and matched literary elements.

3. The method as set forth in claim 1 wherein the annotations include one or more annotations selected from the group consisting of humor, imagery, adventure, character, character mood, and suspense.

4. The method as set forth in claim 3 wherein the humor annotation is further classified as a type of humor selected from the group consisting of malapropism, pun, polemics, dark, silly, political, genderist, sarcasm, epigrammatic, exaggeration, irony, situational, and satire.

5. The method as set forth in claim 1 wherein at least one literary element annotation weight is normalized across a category in which the literary element has been categorized.

6. 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.

7. The method as set forth in claim 1 further comprising, prior to the performing natural language processing and deep semantic analysis, automatically breaking by a computer system the digital work of literature into segments.

8. The method as set forth in claim 7 wherein the breaking into segments is performed according to one or more rules selected from the group consisting of proportionally dividing the entire digital work of literature into a fixed number of segments and dividing the entire digital work of literature into segments according to fixed lengths of the segments.

9. The method as set forth in claim 2 wherein the correlating and matching further comprises at least one step selected from the group consisting of identifying one or more plot devices, associating plot devices with one or more literary elements, and identifying a theme for a plot device.

10. The method as set forth in claim 2 wherein the correlating and matching comprises assigning to each literary element one or more attributes selected from the group consisting of level of importance within the segment, level of prevalence within the segment, number of parties in a relationship to which the literary element pertains in the segment, and number of plot devices in the segment to which the literary element pertains.

11. The method as set forth in claim 10 wherein the correlating and matching further comprises calculating a segment-bound overall weight for a literary element within the segment by applying a mathematical weighting function to the one or more attributes.

12. The method as set forth in claim 11 wherein the correlating and matching further comprises calculating an overall weight for a literary element within a plurality of segments by applying a mathematical weighting function to one or more segment-bound overall weights.

13. The method as set forth in claim 10 wherein the correlating and matching further comprises calculating an overall weight for a literary element within the entire digital work of literature by applying a mathematical weighting function to the one or more attributes.