Computational recognition and comprehension of humor in the context of a general error investigation system

Humor is a creative, ubiquitous, and powerful communication strategy, yet it is currently challenging for computers to correctly identify instances of humor, let alone understand it. In this thesis I develop a computational model of humor based on error identification and resolution, as well as methods for understanding the mental trajectory required for successful humor appreciation. An infrastructure for constructing humor detectors based on this theory is implemented in the context of a general error handling and investigation system for the Genesis story-understanding system. The computational model consists of a series of Experts that quantify important story elements such as allyship, harm to characters, character traits, karma, morbidity, contradiction, and unexpected events. Due to the homogeneous structure of their interactions, Experts using different methodologies such as simulation, Bayesian reasoning, neural nets, or symbolic reasoning can all interact, share findings of interest, and suggest reasons for each other's issues through this system. This system of Experts can identify the resolvable narrative laws that drive humor, therefore they are also able to discover unintentional problems within narratives. I have additionally demonstrated successful quantification of indicators of effective human engagement with narrative such as suspense, attention span length, attention density, and moments of insight. Variations in Expert parameters account for different senses of humor in individuals. This new scope of understanding allows Genesis to help authors search their narratives to determine if higher level narrative mechanics are well executed or not, a crucial role usually reserved for a human editor. By successfully demonstrating a framework for computational recognition and comprehension of humor, I have begun to show that computers are capable of sharing an ability previously considered an exclusively human quality.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Taylor, Ada (Ada V.)

Advisor dc:contributor.advisor

• Patrick H. Winston.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119564

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119564