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All Purpose Textual Data Information Extraction, Visualization and Querying

Abstract Since the advent of the internet and even more after social media platforms, the explosive growth of textual data and its availability has made analysis a tedious task. Information extraction systems are available but are generally too specific and often only extract certain kinds of information they deem necessary and extraction worthy. Using data visualization theory and fast, interactive querying methods, leaving out information might not really be necessary. This thesis explores textual data visualization techniques, intuitive querying, and a novel approach to all-purpose textual information extraction to encode large text corpus to improve human understanding of the information present in textual data.

This thesis presents a modifie... (more)
Created Date 2018
Contributor Hashmi, Syed Usama (Author) / Bansal, Ajay (Advisor) / Bansal, Srividya (Committee member) / Gonzalez Sanchez, Javier (Committee member) / Arizona State University (Publisher)
Subject Computer science / Natural Language Processing / Natural Language Understanding / Text Mining / Text Querying / Text Visualization
Type Masters Thesis
Extent 148 pages
Language English
Note Masters Thesis Software Engineering 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis