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,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,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. |
tech,16-12-J05-1003,bq |
Although the experiments in this article are on
<term>
natural language parsing ( NLP )
</term>
, the
<term>
approach
</term>
should be applicable to many other
<term>
NLP problems
</term>
which are naturally framed as
<term>
ranking tasks
</term>
, for example ,
<term>
speech recognition
</term>
,
<term>
machine translation
</term>
, or
<term>
natural language generation
</term>
.
|
#8952
Although the experiments in this article are on natural language parsing (NLP), theapproach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation. |
model,7-7-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>
.
|
#8806
The method combined the log-likelihood under abaseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model. |
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%. |
tech,15-10-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>
.
|
#8902
Experiments show significant efficiency gains for the new algorithm over the obvious implementation of theboosting approach. |
tech,13-5-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>
.
|
#8772
We introduce a new method for the reranking task, based on theboosting approach to ranking problems described in Freund et al. (1998). |
tech,9-9-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>
.
|
#8870
The article also introduces a new algorithm for theboosting approach which takes advantage of the sparsity of the feature space in the parsing data. |
tech,3-6-J05-1003,bq |
We apply the
<term>
boosting method
</term>
to
<term>
parsing
</term>
the
<term>
Wall Street Journal treebank
</term>
.
|
#8789
We apply theboosting method to parsing the Wall Street Journal treebank. |
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. |
other,12-7-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>
.
|
#8811
The method combined the log-likelihood under a baseline model (that ofCollins [ 1999 ]) with evidence from an additional 500,000 features over parse trees that were not included in the original model. |
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. |
tech,21-11-J05-1003,bq |
We argue that the method is an appealing alternative — in terms of both simplicity and efficiency — to work on
<term>
feature selection methods
</term>
within
<term>
log-linear ( maximum-entropy ) models
</term>
.
|
#8926
We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work onfeature selection methods within log-linear (maximum-entropy) models. |
other,19-4-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 .
|
#8729
The strength of our approach is that it allows a tree to be represented as an arbitrary set offeatures, 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. |
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. |
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,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. |
measure(ment),6-8-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 % .
|
#8841
The new model achieved 89.75%F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%. |