EVL PhD candidate, Yiwen Sun presents her thesis "Articulate: Creating Meaningful Visualizations from Natural Language."

Thesis Advisory Committee: Barbara Di Eugenio, Andrew E. Johnson, Jason Leigh, Tom Peterka, and Luc Renambot

Abstract
While many visualization tools exist that offer sophisticated functions for charting complex data, they still expect users to possess a high degree of expertise in wielding the tools to create an effective visualization. Unfortunately the users of such tools are often lay people or domain experts with marginal knowledge of graphic design and visualization techniques. When exploring data, they typically know what questions they want to ask, but often do not know, or do not have the time to learn, how to express these questions in a form that is suitable for a given visualization tool. To facilitate the use of the advanced visualization tools, I propose an automated visualization framework: Articulate. The goal is to provide a streamlined experience to non-expert users, allowing them to focus on using the visualizations effectively to generate new findings. Articulate, is an approach toward enabling non-visualization experts to leverage advanced visualization techniques through the use of natural language as the primary interface for crafting visualizations. The main challenge in this research is in determining how to translate imprecise verbal queries into precise and meaningful visualizations. While the initial prototype focuses on producing information visualizations, it will also be demonstrated how the approach can be applied to scientific domain as well as the details of the user studies that validate the approach. This model could potentially reduce the learning curve necessary for effective use of visualization tools, and thereby expand the population of users who can successfully conduct visual analysis.