A88-1016 |
the following parameters : * The
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sampling
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frequency for generated frames
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A00-2005 |
k bootstrap replicates of L by
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sampling
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m items from with replacement
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A97-1015 |
domains . We also find the small
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sampling
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problem in this experiment .
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A00-2040 |
alternative , based on Monte Carlo
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sampling
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of the rules . For each error
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A97-1015 |
In other words , it is a small
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sampling
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problem , which can be seen in
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C00-1010 |
derivations are simply removed from the
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sampling
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distribution . After sampling
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A00-2040 |
with a ` lazy ' strategy with
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sampling
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size 5 ( lazy ( 5 ) + in figure
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A00-2009 |
distribution is created by randomly
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sampling
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349 sense-tagged examples from
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A00-2040 |
for an algorithm based on rule
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, this effect is much less severe
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A00-2040 |
required . We experimented with
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sampling
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sizes 5 and 10 . As CPU requirements
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A00-2040 |
for the ` lazy ' algorithm with
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size 5 , show that the phoneme
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C00-1010 |
sampling distribution . After
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sampling
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a large number of random derivations
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A92-1037 |
process was checked by random
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, and 92 % of the links was recognized
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C00-1010 |
which can only be enforced by
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valid representations from the
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A00-1030 |
by Philip Resnik , using some
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tools that he developed . The
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C00-1011 |
will not use Bod 's Monte Carlo
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technique from complete derivation
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C00-1011 |
parse both by means of Monte Carlo
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sampling
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and by means of Viterbi n-best
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A88-1016 |
dictionary is specific to the
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sampling
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frequency used ( 8 , 10 or 16kHz
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A00-2041 |
large space efficiently by first
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sampling
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widely and shal - lowly , and
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A00-1030 |
of an explicit determiner . 3.2
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Sampling
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rule performance The morphological
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