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Abstract:
We introduce a new type of Self-Organizing Map (SOM) to
navigate in the Semantic Space of large text collections. We
propose a ``hyperbolic SOM'' (HSOM) based on a regular
tesselation of the hyperbolic plane, which is a non-euclidean
space characterized by constant negative gaussian curvature. The
exponentially
increasing size of a neighborhood around a point in hyperbolic
space provides more freedom to map the complex information space
arising from language into spatial relations. We describe
experiments, showing that the HSOM can successfully be applied to
text categorization tasks and yields results comparable to other
state-of-the-art methods.
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