  The goal of this research was to find a way to extend the capabilities of
computers through the processing of language in a more human way, and present
applications which demonstrate the power of this method. This research presents
a novel approach, Rhetorical Analysis, to solving problems in Natural Language
Processing (NLP). The main benefit of Rhetorical Analysis, as opposed to
previous approaches, is that it does not require the accumulation of large sets
of training data, but can be used to solve a multitude of problems within the
field of NLP. The NLP problems investigated with Rhetorical Analysis were the
Author Identification problem - predicting the author of a piece of text based
on its rhetorical strategies, Election Prediction - predicting the winner of a
presidential candidate's re-election campaign based on rhetorical strategies
within that president's inaugural address, Natural Language Generation - having
a computer produce text containing rhetorical strategies, and Document
Summarization. The results of this research indicate that an Author
Identification system based on Rhetorical Analysis could predict the correct
author 100% of the time, that a re-election predictor based on Rhetorical
Analysis could predict the correct winner of a re-election campaign 55% of the
time, that a Natural Language Generation system based on Rhetorical Analysis
could output text with up to 87.3% similarity to Shakespeare in style, and that
a Document Summarization system based on Rhetorical Analysis could extract
highly relevant sentences. Overall, this study demonstrated that Rhetorical
Analysis could be a useful approach to solving problems in NLP.
