Self-organizing maps
TOPIC ISLANDS—a wavelet-based text visualization system
Proceedings of the conference on Visualization '98
The Shape of Shakespeare: Visualizing Text using Implicit Surfaces
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Multi-Faceted Insight Through Interoperable Visual Information Analysis Paradigms
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
BiblioMapper: A Cluster-Based Information Visualization Technique
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
The Sunflower Visual Metaphor, a New Paradigm for Dimensional Compression
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Motion map: image-based retrieval and segmentation of motion data
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Visualizing Live Text Streams Using Motion and Temporal Pooling
IEEE Computer Graphics and Applications
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HNC Software, Inc. has developed a system called DEPICT for visualizing the information content of large textual corpora. The system is built around two separate neural network methodologies: context vectors and self-organizing maps. Context vectors (CVs) are high dimensional information representations that encode the semantic content of the textual entities they represent. Self-organizing maps (SOMs) are capable of transforming an input, high dimensional signal space into a much lower (usually two or three) dimensional output space useful for visualization. Neither process requires human intervention, nor an external knowledge base. Together, these neural network techniques can be utilized to automatically identify the relevant information themes present in a corpus, and present those themes to the user in a intuitive visual form.