The
<term>
model
</term>
gives an
<term>
F-measure improvement
</term>
of [ ? ] 1.25 % beyond the
<term>
base parser
</term>
, and an [ ? ] 0.25 % improvement beyond the
<term>
Collins ( 2000 ) reranker
</term>
.
#5528The model gives an F-measure improvement of [?] 1.25% beyond the base parser, and an [?] 0.25% improvement beyond the Collins (2000) reranker.
model,8-5-H05-1064,ak
As a case study , we apply the
<term>
model
</term>
to
<term>
parse reranking
</term>
.
#5522As a case study, we apply the model to parse reranking.
model,1-3-H05-1064,ak
The
<term>
model
</term>
learns to automatically make these assignments based on a
<term>
discriminative training criterion
</term>
.
#5471The model learns to automatically make these assignments based on a discriminative training criterion.
model,5-4-H05-1064,ak
<term>
Training
</term>
and
<term>
decoding
</term>
with the
<term>
model
</term>
requires summing over an exponential number of hidden-variable assignments : the required summations can be computed efficiently and exactly using
<term>
dynamic programming
</term>
.
#5490Training and decoding with the model requires summing over an exponential number of hidden-variable assignments: the required summations can be computed efficiently and exactly using dynamic programming.