Date of Submission
Spring 5-18-2020
Degree Type
Thesis
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
Committee Chair/First Advisor
Dan Lo
Track
Big Data
Chair
Coskun Cetinkaya
Committee Member
Junggab Son
Committee Member
Selena He
Abstract
A text is a set of words conveying a particular semantic based on their order, representation and structure. Those elements can be associated through a different set of interpretations, based on frequency and proportionality. The problem with context is that numbers do not help understand the semantics and fall short to convey the message of the text. The graphical representation of text semantics focuses on the conversion of text to images. Contrarily to word clouds that simply produce frequency mapping of words within the text and topic models that essentially give context to word frequencies and proportionalities, images keep intact the semantic and the context of the words in the text. They provide a deeper understanding and can be better interpreted. Models such as AttnGAN already exist to convert text into images with a certain level of success, but there has not been work done concerning the conversion of long and complex texts in an image or a set of images. The goal of this analysis is to first, provide an understanding of how we divide the text in bits that improve the resulting image and how does the summarization methodology affect the image result.
Included in
Graphics and Human Computer Interfaces Commons, Numerical Analysis and Scientific Computing Commons, Theory and Algorithms Commons