Department of Software Technology
Vienna University of Technology
CIA's view of the world and what neural networks learn from it:
A comparison of geographical document space representation metaphors
Abstract:
Text collections may be regarded as an almost perfect application arena
for unsupervised neural networks. This because many operations computers
have to perform on text documents are classification tasks based on noisy
patterns. In particular we rely on self-organizing maps which produce a map
of the document space after their training process. From geography,
however, it is known that maps are not always the best way to represent
information spaces. For most applications it is better to provide a
hierarchical view of the underlying data collection in form of an atlas
where starting from a map representing the complete data collection
different regions are shown at finer levels of granularity. Using an
atlas, the user can easily ``zoom'' into regions of particular interest
while still having general maps for overall orientation. We show that a
similar display can be obtained by using hierarchical feature maps to
represent the contents of a document archive. These neural networks have
a layered architecture where each layer consists of a number of individual
self-organizing maps. By this, the contents of the text archive may be
represented at arbitrary detail while still having the general maps
available for global orientation.
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Comments: rauber@ifs.tuwien.ac.at