Jigsaw: investigative analysis on text document collections through visualization

This article describes the Jigsaw system for helping investigative analysis across collections of text documents. Jigsaw provides multiple visualizations of the documents and the entities within them to help investigators discern embedded stories and plots. Our early focus within Jigsaw has not been on legal documents and E-discovery, but we feel that the system may have potential in these areas as well. This article illustrates Jigsaw’s views and operations using Enron email archives as example documents

A Text Visualization Method Based on A Label Cloud

An important direction of visualization technology research is the visualization of text data. Based on the characteristics of text information visualization, a text visualization method based on label cloud is studied, which puts forward the data index, complexity index and identification index to describe visualization, and calculates the weight of the total evaluation score by the calculation formula of three kinds of indexes. Through the visualization experiments of various kinds of text information, the results show that the method has some validity in visual measurement, and the index values at all levels are also relevant

Generating Music from Literature

We present a system, TransProse, that automatically generates musical pieces from text. TransProse uses known relations between elements of music such as tempo and scale, and the emotions they evoke. Further, it uses a novel mechanism to determine sequences of notes that capture the emotional activity in the text. The work has applications in information visualization, in creating audio-visual e-books, and in developing music apps

Large Text Database Visualization

A system for intuitively visualizing, searching, and querying the contents of large text databases is under development at TASC. This text visualization (TEXTVlZ) system will generate a map-like representation of the contents of a document database which will allow users to visualize how the documents interrelate in terms of their conceptual content. This method of visualizing text databases will allow users to classify documents by the relative similarity of their meaning as well as to discover and to explore conceptual differences between clusters of documents

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

Multimodal Explanations: Justifying Decisions and Pointing to the Evidence

Deep models that are both effective and explainable are desirable in many settings; prior explainable models have been unimodal, offering either image-based visualization of attention weights or text-based generation of post-hoc justifications. We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths. We collect two new datasets to define and evaluate this task, and propose a novel model which can provide joint textual rationale generation and attention visualization. Our datasets define visual and textual justifications of a classification decision for activity recognition tasks (ACT-X) and for visual question answering tasks (VQA-X). We quantitatively show that training with the textual explanations not only yields better textual justification models, but also better localizes the evidence that supports the decision. We also qualitatively show cases where visual explanation is more insightful than textual explanation, and vice versa, supporting our thesis that multimodal explanation models offer significant benefits over unimodal approaches.Comment: arXiv admin note: text overlap with arXiv:1612.0475

Digital humanities is text heavy, visualization light, and simulation poor

This article examines the question of whether Digital Humanities has given too much focus to text over non-text media and provides four major reasons to encourage more non-text-focused research under the umbrella of Digital Humanities. How could Digital Humanities engage in more humanities-oriented rhetorical and critical visualization, and not only in the development of scientific visualization and information visualization