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Project Admins:

• Tanya Clement

[Please note: This downloads includes the ProseVis.jar (executable). Browse other downloads for TestDocuments.zip (including Tender Buttons and the New England Cook Book).]

Project Director: Tanya Clement
Lead Developer: Ankita Goel
Developers: Megan Monroe and Austin Myers

ProseVis is a visualization tool developed as part of a use case supported by the Andrew W. Mellon Foundation through a grant titled "SEASR Services," in which we seek to identify other features than the "word" to analyze texts. These features comprise sound including parts-of-speech, accent, phoneme, stress, tone, break index.

ProseVis allows a reader to map the features extracted from OpenMary (http://mary.dfki.de/) Text-to-speech System and predictive classification data to the "original" text. We developed this project with the ultimate goal of facilitating a reader's ability to analyze and disseminate the results in human readable form. Research has shown that mapping the data to the text in its original form allows for the kind of human reading that literary scholars engage: words in the context of phrases, sentences, lines, stanzas, and paragraphs (Clement 2008). Recreating the context of the page not only allows for the simultaneous consideration of multiple representations of knowledge or readings (since every reader’s perspective on the context will be different) but it also allows for a more transparent view of the underlying data. If a human can see the data (the syllables, the sounds, the parts-of-speech) within the context in which they are used to reading, with the data mapped back onto the full text, then the reader is empowered within this familiar context to read what might otherwise be an unfamiliar representation tabular representation of the text. For these reasons, we developed ProseVis as a reader interface to allow scholars to work with the data in a language or context in which we are used to saying things about the world.

ProseVis has been developed in a two-stage process, first as VerseVis: Visualizing Spoken Language Features in Text by graduate students Christine Lu, Leslie Milton, and Austin Myers as part of a graduate course in visualization with Ben Shneiderman at the University of Maryland, College Park. Megan Monroe further developed the prototype as ProseVis under the auspices of this grant.

Downloads available here include: ProseVis.jar (executable) and TestDocuments.zip (including Tender Buttons and the New England Cook Book)

Project Participants: Tanya Clement, Assistant Professor, School of Information, University of Texas at Austin
Loretta Auvil, Senior Project Coordinator at Illinois Informatics Institute at University of Illinois, Urbana-Champaign
David Tcheng, I3 Research Scientist, SEASR, University of Illinois, Urbana-Champaign
Megan Monroe, PhD Candidate, Computer Science Department, University of Maryland, College Park
Ankita Goel, MA Candidate, Computer Science Department, University of Texas at Austin
Austin Myers, PhD Candidate, Computer Science Department, University of Maryland, College Park