Graphical Representation of Text Semantics

<p>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.</p>

Name thesis:degree_name

Master of Science in Computer Science (MSCS)

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2020

Author dc:creator

• Tiba Fossoh, Karl Kevin

Contributors dc:contributor

• Dan Lo
• Coskun Cetinkaya
• Junggab Son

• Summarization
• LSTM
• GAN
• LDA
• Image Realism
• Coherence
• Graphics and Human Computer Interfaces
• Numerical Analysis and Scientific Computing
• Theory and Algorithms

Repository record dc:identifier

https://digitalcommons.kennesaw.edu/cs_etd/43

OAI identifier oai:identifier

oai:digitalcommons.kennesaw.edu:cs_etd-1037