Articulate: Creating Meaningful Visualizations from Natural Language

View Sample PDF

Author(s): Yiwen Sun (Electronic Visualization Laboratory, University of Illinois at Chicago, USA), Jason Leigh (Electronic Visualization Laboratory, University of Illinois at Chicago, USA), Andrew Johnson (Electronic Visualization Laboratory, University of Illinois at Chicago, USA)and Barbara Di Eugenio (Natural Language Processing Laboratory, University of Illinois at Chicago, USA)
Copyright: 2014
Pages: 18
Source title: Innovative Approaches of Data Visualization and Visual Analytics
Source Author(s)/Editor(s): Mao Lin Huang (University of Technology, Sydney, Australia)and Weidong Huang (CSIRO, Australia)
DOI: 10.4018/978-1-4666-4309-3.ch011

Keywords: Data Analysis & Statistics / Data Mining and Databases / Information Science Reference / Library & Information Science

Purchase

View Articulate: Creating Meaningful Visualizations from Natural Language on the publisher's website for pricing and purchasing information.

Abstract

This chapter presents an approach to enable non-visualization experts to craft advanced visualizations through the use of natural language as the primary interface. The main challenge in this research is in determining how to translate imprecise verbal queries into effective visualizations. To demonstrate the viability of the concept, the authors developed and evaluated a prototype, Articulate, which allows users to simply ask the computer for questions about their data and have it automatically generate visualizations that answer these questions. The authors discovered that by relieving the user of the burden of learning how to use a complex interface, they enable them to focus on articulating better scientific questions and wasting less time in producing unintended visualizations.

The IRMA Community

Research IRM

Articulate: Creating Meaningful Visualizations from Natural Language

Purchase

Abstract

Related Content

IRMA Sponsors