tech,39-12-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>
.
|
#8975
Although the experiments in this article are on natural language parsing (NLP), the approach 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. |
tech,40-4-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 .
|
#8750
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 agenerative model which takes these features into account. |
other,23-9-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>
.
|
#8884
The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in theparsing data. |
other,20-5-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>
.
|
#8779
We introduce a new method for the reranking task, based on the boosting approach to ranking problems described inFreund 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,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. |
tech,43-12-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>
.
|
#8979
Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, ornatural language generation. |
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,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,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. |
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. |
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. |
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. |
tech,30-12-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>
.
|
#8966
Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed asranking tasks, for example, speech recognition, machine translation, or natural language generation. |
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). |
other,23-12-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>
.
|
#8959
Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many otherNLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation. |
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. |
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,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. |