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Abstract:
Singular value decomposition (SVD) can be viewed as a method
for unsupervised training of a network that associates two classes
of events reciprocally by linear connections through a single
hidden layer. SVD was used to learn and represent relations among
very large numbers of words (20k-60k) and very large numbers of
natural text passages (1k-70k) in which they occurred. The result
was 100-350 dimensional "semantic spaces" in which any trained or
newly added word or passage could be represented as a vector, and
similarities were measured by the cosine of the contained angle
between vectors. Good accuracy in simulating human judgments and
behaviors has been demonstrated by performance on multiple-choice
vocabulary and domain knowledge tests, emulation of expert essay
evaluations, and in several other ways. Examples are also given of
how the kind of knowledge extracted by this method can be
applied.
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