W13-3509 |
approach was based on a naive
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hard matching
|
between word lemmas . Below we
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W09-0204 |
In addi - tion , the problem of
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hard matching
|
can be alleviated by processing
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S13-1022 |
method as the NLPM models . Here ,
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hard matching
|
was performed , where matching
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P09-1106 |
Another reason is that TER uses
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hard matching
|
in computing edit distance .
|
C04-1078 |
that is not matched by any of the
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hard matching
|
pattern rules . Only those fields
|
C04-1078 |
bootstrapping framework in which soft and
|
hard matching
|
pattern rules are combined in
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D15-1210 |
refer to its loss function as
|
hard matching
|
: AHM ( a1 , a2 ) = 1 −
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P07-1026 |
Tree Path feature is sensitive to
|
hard matching
|
between any two sub-trees without
|
P07-1026 |
However , it only carries out
|
hard matching
|
, which may lead to over-fitting
|
C04-1078 |
variations . The reason is that
|
hard matching
|
techniques result in relatively
|
D15-1210 |
developing a soft version of the
|
hard matching
|
loss function because this will
|
P03-2040 |
alignment , if we do more than
|
hard matching
|
of punctuations and take into
|
D15-1210 |
BERKELEY BLEU . " HM " denotes the
|
hard matching
|
loss function , " SM " denotes
|
E95-1010 |
of byte-length ratio measures ,
|
hard matching
|
of numbers , string comparisons
|
P07-1026 |
convolution tree kernel only carries out
|
hard matching
|
, so it fails to handle similar
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C04-1078 |
induction techniques based on
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hard matching
|
( i.e. , strict slot-by-slot
|
P06-2074 |
Xiao et al. ( 2004 ) stated that
|
hard matching
|
techniques tend to have low recall
|
C04-1078 |
<title> Cascading Use of Soft and
|
Hard Matching
|
Pattern Rules for Weakly Supervised
|