Representing Textual Passages as Graphs to Support Question Answering
Representing Textual Passages as Graphs to Support Question Answering
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Date
2020-12-01
Authors
Christian, Tyler
Journal Title
Journal ISSN
Volume Title
Publisher
University Honors College Middle Tennessee State University
Abstract
As technology and its capabilities are profoundly increasing, the means of
communication between humans has become immensely easier. Technological advances
are ever more applicable with Artificial Intelligence and the processing of natural
language data. However, a problem that is worth pursuing is the concept that machines
processing natural language data have little comprehension during question answering.
Machines often have difficulty understanding information because data are usually not
represented in a comprehensible manner. To take a step toward solving this problem, this
thesis will explore a new, automated way to represent any short news passage in the form
of a graph. Such graphs are useful because they represent the most amount of information
while being compact and leading to accurate, efficient answers. The ability to see
relationships throughout entire passages, having properties that make question answering
possible, and being able to graph any short news passage bring immense value to this
project. This project carries significance because of the fact that it is interfaceable with
other systems, simplifying work by serving as a driver and being able to be combined
with additional tools. The Natural Language Tool Kit, Neo4j Database Software,
Stanford Core Natural Language Processing, and the Python programming language are
all tools that were used in completing this project.
Description
Keywords
College of Basic and Applied Sciences,
computer,
computer science,
natural language processing,
nlp,
graphs,
question answering,
question/answering,
artificial intelligence,
ai