Language and Computation advanced course:
Linguistic Information Visualization.

• Gerald Penn ()
• Sheelagh Carpendale ()

Course material: carpendale_penn.pdf

Much of what computational linguists fall back upon to improve natural language processing and model language "understanding" is structure that has, at best, only an indirect attestation in observable data. The sheer complexity of these structures, and the observable patterns on which they are based, however, usually limits their accessibility, often even to the researchers creating or studying them. Traditional statistical graphs and custom-designed data illustrations fill the pages of CL papers, providing insight into linguistic and algorithmic structures, but visual 'externalizations' such as these are almost exclusively used in CL for presentation and explanation.

Visualizations can also be used as an aid in the process of research itself. There are special statistical methods, falling under the rubric of "exploratory data analysis", and visualization techniques just for this purpose, in fact, but these are not widely used or even known in CL. These novel data visualization techniques offer the potential for creating new methods that reveal structure and detail in data. Visualization can provide new methods for interacting with large corpora, complex linguistic structures, and can lead to a better understanding of the states of stochastic processes.

Instructed by a team of computational linguists and information visualization researchers, this tutorial will bridge computational linguistic and information visualization expertise, providing attendees with a basis from which they can begin to leverage information visualization in their own research. It will equip participants with: - An understanding of the importance and applicability of information visualization techniques to computational linguistics research; - Knowledge of the basic principles of information visualization theory; - The ability to identify appropriate visualization software and techniques that are available for immediate use and for prototyping; - A working knowledge of research to date in the area of linguistic visualization.

This tutorial will be an extended version of the 3-hour tutorial offered at ACL-2008, which had 25 attendees. The instructors have previously taught portions of the content in advanced undergraduate and graduate courses as well. Students are expected to have a solid background in computational linguisics. No experience with visualization is required.

TUTORIAL OUTLINE

Day 1: Introduction; Information Visualization Theory (representational theory, cognitive psychology, preattentive processing, interaction & animation, assessing and validating visualizations)

Days 2 and 4: Review of Linguistic Visualizations (document content visualizations, text collection analysis, literary analysis, streaming data visualization, convergence of linguistic data and social network analysis, corpora exploration, visualization uncertainty in statistical NLP output, linguistic analysis, visualization of speech data)

Day 3: Tools for Visualization (software solutions: Excel, Tableau, Spotfire, programming toolkits: prefuse, processing, flare, InfoVis Toolkit, online tools: ManyEyes, Swivel, collaborative visualization tools in development)

Day 5: Case Study: Visualization for Statistical MT; Open Research Problems (CL problems that could benefit from visualization, Visualization of language areas that need CL expertise); Closing