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We used
recurrent back-propagation for six time steps
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with the sememe
vector as the desired output for the last
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3 time steps.
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The
network creates semantic attractors.
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Each word meaning is a point in semantic
space and
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has
its own basin of attraction.
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Damage
to the sememe or clean-up units can change
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the
boundaries of the attractors.
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This
explains semantic errors. Meanings fall into a
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neighboring
attractor.
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Damage
to the bottom-up input can change the initial
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conditions
for the attractors.
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This
explains why early damage can cause semantic errors.
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