Visualizing the semantic content of large text databases using text maps

A methodology for generating text map representations of the semantic content of text databases is presented. Text maps provide a graphical metaphor for conceptualizing and visualizing the contents and data interrelationships of large text databases. Described are a set of experiments conducted against the TIPSTER corpora of Wall Street Journal articles. These experiments provide an introduction to current work in the representation and visualization of documents by way of their semantic content

Text visualization techniques: Taxonomy, visual survey, and community insights

Figure 1: The web-based user interface of our visual survey called Text Visualization Browser. By using the interaction panel on the left hand side, researchers can look for specific visualization techniques and filter out entries with respect to a set of categories (cf. the taxonomy given in Sect. 3). Details for a selected entry are shown by clicking on a thumbnail image in the main view. The survey contains 141 categorized visualization techniques by January 19, 2015. Text visualization has become a growing and increasingly impor-tant subfield of information visualization. Thus, it is getting harder for researchers to look for related work with specific tasks or vi-sual metaphors in mind. In this paper, we present an interactive visual survey of text visualization techniques that can be used for the purposes of search for related work, introduction to the subfield and gaining insight into research trends. We describe the taxonomy used for categorization of text visualization techniques and com-pare it to approaches employed in several other surveys. Finally, we present results of analyses performed on the entries data

Introducing legacy program scripting to molecular biology toolkit (MBT)

Successful navigating of the ever-changing landscape of molecular visualization programs requires a common thread that can offer users an oasis from the maelstrom of new languages, proprietary applications, and miniscule software life-cycles. Crossapplication scripting remains the benchmark, allowing scientists and researchers to speak a common tongue. The introduction of a new, powerful visualization language, Molecular Biology Toolkit (MBT), has tempted many users to abandon previous methodologies and adopt a new mode of research. MBT, however, is not without drawbacks. Its lack of scripting capabilities creates unmanageable complexity for unsophisticated end-users, namely those without the ability to program. MBT, thus, lacks the basic handholds for its widespread acceptance in the molecular visualization community. As a toolkit package without its own mode of execution, its design challenges users to develop their own customized features and applications. However, able to contribute as a text based virtual molecular collection or a fully rendered 3D molecular representation, MBT has the tools researchers want in a new visualization program. Using JavaCC to parse legacy commands and in turn executing MBT methods all from a single, simple command, I have reintroduced scripting to the modern molecular visualization landscape. Combining these two programs, this project takes steps to encourage the exciting molecular manipulations capable in MBT while bridging to a friendly, user-centric scripting patterns required by end-users not entrenched in software development

Visualizing Incongruity: Visual Data Mining Strategies for Modeling Humor in Text

The goal of this project is to investigate the use of visual data mining to model verbal humor. We explored various means of text visualization to identify key featrues of garden path jokes as compared with non jokes. With garden path jokes one interpretation is established in the setup but new information indicating some alternative interpretation triggers some resolution process leading to a new interpretation. For this project we visualize text in three novel ways, assisted by some web mining to build an informal ontology, that allow us to see the differences between garden path jokes and non jokes of similar form. We used the results of the visualizations to build a rule based model which was then compared with models from tradtitional data mining toi show the use of visual data mining. Additional experiments with other forms of incongruity including visualization of ’shilling’ or the introduction of false reviews into a product review set. The results are very similar to that of garden path jokes and start to show us there is a shape to incongruity. Overall this project shows as that the proposed methodologies and tools offer a new approach to testing and generating hypotheses related to theories of humor as well as other phenomena involving opposition, incongruities, and shifts in classification