Narrative analysis and computational model to predict interestingness of narratives

Authors

Laxman Thapa, University of Texas-Pan American

Date of Award

5-2011

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Emmett Tomai

Second Advisor

Dr. Richard Fowler

Third Advisor

Dr. Laura Grabowski

Abstract

In this research, I present results demonstrating the classification of the specially generated narratives by a machine agent by listening to human subject describing the same sets of the events. These classifications are based on human ratings of interestingness for many different recountings of the same stories. The classification is performed on various features selected after analyzing the different possible feature that affect on the interestingness of narratives. The features were extracted from the surface text as well as from annotations of how each narration relates to the content of the known story. I present the annotation process and resulting corpus, the feature selection, and experimental results for the task of predicting the interestingness.

Comments

Copyright 2011 Laxman Thapa. All Right Reserved.

https://www.proquest.com/dissertations-theses/narrative-analysis-computational-model-predict/docview/875789964/se-2?accountid=7119

Granting Institution

University of Texas-Pan American