tech,2-8-J05-1003,bq |
The new
<term>
model
</term>
achieved 89.75 %
<term>
F-measure
</term>
, a 13 % relative decrease in
<term>
F-measure
</term>
error over the
<term>
baseline model ’s score
</term>
of 88.2 % .
|
#8837
The newmodel achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%. |
other,17-3-J05-1003,bq |
A second
<term>
model
</term>
then attempts to improve upon this initial
<term>
ranking
</term>
, using additional
<term>
features
</term>
of the
<term>
tree
</term>
as evidence .
|
#8706
A second model then attempts to improve upon this initial ranking, using additional features of thetree as evidence. |
tech,1-7-J05-1003,bq |
The
<term>
method
</term>
combined the
<term>
log-likelihood
</term>
under a
<term>
baseline model
</term>
( that of
<term>
Collins [ 1999 ]
</term>
) with evidence from an additional 500,000
<term>
features
</term>
over
<term>
parse trees
</term>
that were not included in the original
<term>
model
</term>
.
|
#8800
Themethod combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model. |
other,16-5-J05-1003,bq |
We introduce a new
<term>
method
</term>
for the
<term>
reranking task
</term>
, based on the
<term>
boosting approach
</term>
to
<term>
ranking problems
</term>
described in
<term>
Freund et al. ( 1998 )
</term>
.
|
#8775
We introduce a new method for the reranking task, based on the boosting approach toranking problems described in Freund et al. (1998). |
measure(ment),14-8-J05-1003,bq |
The new
<term>
model
</term>
achieved 89.75 %
<term>
F-measure
</term>
, a 13 % relative decrease in
<term>
F-measure
</term>
error over the
<term>
baseline model ’s score
</term>
of 88.2 % .
|
#8849
The new model achieved 89.75% F-measure, a 13% relative decrease inF-measure error over the baseline model’s score of 88.2%. |
other,7-2-J05-1003,bq |
The base
<term>
parser
</term>
produces a set of
<term>
candidate parses
</term>
for each input
<term>
sentence
</term>
, with associated
<term>
probabilities
</term>
that define an initial
<term>
ranking
</term>
of these
<term>
parses
</term>
.
|
#8670
The base parser produces a set ofcandidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. |
tech,6-9-J05-1003,bq |
The article also introduces a new
<term>
algorithm
</term>
for the
<term>
boosting approach
</term>
which takes advantage of the
<term>
sparsity of the feature space
</term>
in the
<term>
parsing data
</term>
.
|
#8867
The article also introduces a newalgorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. |
tech,4-5-J05-1003,bq |
We introduce a new
<term>
method
</term>
for the
<term>
reranking task
</term>
, based on the
<term>
boosting approach
</term>
to
<term>
ranking problems
</term>
described in
<term>
Freund et al. ( 1998 )
</term>
.
|
#8763
We introduce a newmethod for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). |
other,16-9-J05-1003,bq |
The article also introduces a new
<term>
algorithm
</term>
for the
<term>
boosting approach
</term>
which takes advantage of the
<term>
sparsity of the feature space
</term>
in the
<term>
parsing data
</term>
.
|
#8877
The article also introduces a new algorithm for the boosting approach which takes advantage of thesparsity of the feature space in the parsing data. |
other,26-4-J05-1003,bq |
The strength of our
<term>
approach
</term>
is that it allows a
<term>
tree
</term>
to be represented as an arbitrary set of
<term>
features
</term>
, without concerns about how these
<term>
features
</term>
interact or overlap and without the need to define a
<term>
derivation
</term>
or a
<term>
generative model
</term>
which takes these
<term>
features
</term>
into account .
|
#8736
The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how thesefeatures interact or overlap and without the need to define a derivation or a generative model which takes these features into account. |
other,45-4-J05-1003,bq |
The strength of our
<term>
approach
</term>
is that it allows a
<term>
tree
</term>
to be represented as an arbitrary set of
<term>
features
</term>
, without concerns about how these
<term>
features
</term>
interact or overlap and without the need to define a
<term>
derivation
</term>
or a
<term>
generative model
</term>
which takes these
<term>
features
</term>
into account .
|
#8755
The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes thesefeatures into account. |
tech,4-4-J05-1003,bq |
The strength of our
<term>
approach
</term>
is that it allows a
<term>
tree
</term>
to be represented as an arbitrary set of
<term>
features
</term>
, without concerns about how these
<term>
features
</term>
interact or overlap and without the need to define a
<term>
derivation
</term>
or a
<term>
generative model
</term>
which takes these
<term>
features
</term>
into account .
|
#8714
The strength of ourapproach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account. |
measure(ment),18-8-J05-1003,bq |
The new
<term>
model
</term>
achieved 89.75 %
<term>
F-measure
</term>
, a 13 % relative decrease in
<term>
F-measure
</term>
error over the
<term>
baseline model ’s score
</term>
of 88.2 % .
|
#8853
The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over thebaseline model ’s score of 88.2%. |
other,12-10-J05-1003,bq |
Experiments show significant efficiency gains for the new
<term>
algorithm
</term>
over the obvious
<term>
implementation
</term>
of the
<term>
boosting approach
</term>
.
|
#8899
Experiments show significant efficiency gains for the new algorithm over the obviousimplementation of the boosting approach. |
other,14-3-J05-1003,bq |
A second
<term>
model
</term>
then attempts to improve upon this initial
<term>
ranking
</term>
, using additional
<term>
features
</term>
of the
<term>
tree
</term>
as evidence .
|
#8703
A second model then attempts to improve upon this initial ranking, using additionalfeatures of the tree as evidence. |
other,21-2-J05-1003,bq |
The base
<term>
parser
</term>
produces a set of
<term>
candidate parses
</term>
for each input
<term>
sentence
</term>
, with associated
<term>
probabilities
</term>
that define an initial
<term>
ranking
</term>
of these
<term>
parses
</term>
.
|
#8684
The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initialranking of these parses. |
other,37-4-J05-1003,bq |
The strength of our
<term>
approach
</term>
is that it allows a
<term>
tree
</term>
to be represented as an arbitrary set of
<term>
features
</term>
, without concerns about how these
<term>
features
</term>
interact or overlap and without the need to define a
<term>
derivation
</term>
or a
<term>
generative model
</term>
which takes these
<term>
features
</term>
into account .
|
#8747
The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define aderivation or a generative model which takes these features into account. |
other,23-7-J05-1003,bq |
The
<term>
method
</term>
combined the
<term>
log-likelihood
</term>
under a
<term>
baseline model
</term>
( that of
<term>
Collins [ 1999 ]
</term>
) with evidence from an additional 500,000
<term>
features
</term>
over
<term>
parse trees
</term>
that were not included in the original
<term>
model
</term>
.
|
#8822
The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000features over parse trees that were not included in the original model. |
tech,8-10-J05-1003,bq |
Experiments show significant efficiency gains for the new
<term>
algorithm
</term>
over the obvious
<term>
implementation
</term>
of the
<term>
boosting approach
</term>
.
|
#8895
Experiments show significant efficiency gains for the newalgorithm over the obvious implementation of the boosting approach. |
tech,2-2-J05-1003,bq |
The base
<term>
parser
</term>
produces a set of
<term>
candidate parses
</term>
for each input
<term>
sentence
</term>
, with associated
<term>
probabilities
</term>
that define an initial
<term>
ranking
</term>
of these
<term>
parses
</term>
.
|
#8665
The baseparser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. |