The proposed method remedies these problems by adjusting the
<term>
parameters
</term>
to maximize the
<term>
accuracy rate
</term>
directly .
#22847The proposed method remedies these problems by adjusting the parameters to maximize the accuracy rate directly.
measure(ment),1-6-C92-1055,ak
The
<term>
accuracy rate
</term>
of
<term>
syntactic disambiguation
</term>
is raised from 46.0 % to 60.62 % by using this novel approach .
#22898The accuracy rate of syntactic disambiguation is raised from 46.0% to 60.62% by using this novel approach.
other,9-2-C92-1055,ak
Owing to the problem of insufficient
<term>
training data
</term>
and
<term>
approximation error
</term>
introduced by the
<term>
language model
</term>
, traditional
<term>
statistical approaches
</term>
, which resolve
<term>
ambiguities
</term>
by indirectly and implicitly using
<term>
maximum likelihood method
</term>
, fail to achieve high performance in real applications .
#22801Owing to the problem of insufficient training data and approximation error introduced by the language model, traditional statistical approaches, which resolve ambiguities by indirectly and implicitly using maximum likelihood method, fail to achieve high performance in real applications.
model,14-2-C92-1055,ak
Owing to the problem of insufficient
<term>
training data
</term>
and
<term>
approximation error
</term>
introduced by the
<term>
language model
</term>
, traditional
<term>
statistical approaches
</term>
, which resolve
<term>
ambiguities
</term>
by indirectly and implicitly using
<term>
maximum likelihood method
</term>
, fail to achieve high performance in real applications .
#22806Owing to the problem of insufficient training data and approximation error introduced by the language model , traditional statistical approaches, which resolve ambiguities by indirectly and implicitly using maximum likelihood method, fail to achieve high performance in real applications.
other,26-4-C92-1055,ak
To make the proposed
<term>
algorithm
</term>
robust , the possible variations between the
<term>
training corpus
</term>
and the real tasks are also taken into consideration by enlarging the
<term>
separation margin
</term>
between the correct
<term>
candidate
</term>
and its competing members .
#22877To make the proposed algorithm robust, the possible variations between the training corpus and the real tasks are also taken into consideration by enlarging the separation margin between the correct candidate and its competing members.