Although the experiments in this article are on
<term>
natural language parsing ( NLP )
</term>
, the approach 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>
.
#8337Although 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,39-12-J05-1003,ak
Although the experiments in this article are on
<term>
natural language parsing ( NLP )
</term>
, the approach 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>
.
#8340Although 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.
measure(ment),6-8-J05-1003,ak
The new
<term>
model
</term>
achieved 89.75 %
<term>
F-measure
</term>
, a 13 % relative decrease in
<term>
F-measure error
</term>
over the
<term>
baseline model ’s
</term>
score of 88.2 % .
#8206The new model achieved 89.75%F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%.
other,23-7-J05-1003,ak
The method combined the
<term>
log-likelihood under a baseline model
</term>
( that of Collins [ 1999 ] ) 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>
.
#8187The 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.
other,10-4-J05-1003,ak
The strength of our approach 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 .
#8085The strength of our approach is that it allows atree 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.
other,37-4-J05-1003,ak
The strength of our approach 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 .
#8112The 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.
model,40-4-J05-1003,ak
The strength of our approach 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 .
#8115The 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,14-3-J05-1003,ak
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 .
#8068A second model then attempts to improve upon this initial ranking, using additionalfeatures of the tree as evidence.
other,30-12-J05-1003,ak
Although the experiments in this article are on
<term>
natural language parsing ( NLP )
</term>
, the approach 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>
.
#8331Although 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.
other,16-2-J05-1003,ak
The
<term>
base parser
</term>
produces a set of
<term>
candidate parses
</term>
for each
<term>
input sentence
</term>
, with associated
<term>
probabilities
</term>
that define an initial
<term>
ranking
</term>
of these
<term>
parses
</term>
.
#8044The base parser produces a set of candidate parses for each input sentence, with associatedprobabilities that define an initial ranking of these parses.
other,11-2-J05-1003,ak
The
<term>
base parser
</term>
produces a set of
<term>
candidate parses
</term>
for each
<term>
input sentence
</term>
, with associated
<term>
probabilities
</term>
that define an initial
<term>
ranking
</term>
of these
<term>
parses
</term>
.
#8039The base parser produces a set of candidate parses for eachinput sentence, with associated probabilities that define an initial ranking of these parses.
measure(ment),14-8-J05-1003,ak
The new
<term>
model
</term>
achieved 89.75 %
<term>
F-measure
</term>
, a 13 % relative decrease in
<term>
F-measure error
</term>
over the
<term>
baseline model ’s
</term>
score of 88.2 % .
#8214The 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,ak
The
<term>
base parser
</term>
produces a set of
<term>
candidate parses
</term>
for each
<term>
input sentence
</term>
, with associated
<term>
probabilities
</term>
that define an initial
<term>
ranking
</term>
of these
<term>
parses
</term>
.
#8049The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initialranking of these parses.
other,10-3-J05-1003,ak
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 .
#8064A second model then attempts to improve upon this initialranking, using additional features of the tree as evidence.
tech,6-9-J05-1003,ak
The article also introduces a new
<term>
algorithm
</term>
for the
<term>
boosting approach
</term>
which takes advantage of the
<term>
sparsity
</term>
of the
<term>
feature space
</term>
in the
<term>
parsing data
</term>
.
#8232The article also introduces a newalgorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data.
model,2-8-J05-1003,ak
The new
<term>
model
</term>
achieved 89.75 %
<term>
F-measure
</term>
, a 13 % relative decrease in
<term>
F-measure error
</term>
over the
<term>
baseline model ’s
</term>
score of 88.2 % .
#8202The newmodel achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%.
tech,8-10-J05-1003,ak
Experiments show significant efficiency gains for the new
<term>
algorithm
</term>
over the obvious implementation of the
<term>
boosting approach
</term>
.
#8260Experiments show significant efficiency gains for the newalgorithm over the obvious implementation of the boosting approach.
other,7-2-J05-1003,ak
The
<term>
base parser
</term>
produces a set of
<term>
candidate parses
</term>
for each
<term>
input sentence
</term>
, with associated
<term>
probabilities
</term>
that define an initial
<term>
ranking
</term>
of these
<term>
parses
</term>
.
#8035The base parser produces a set ofcandidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses.
other,19-4-J05-1003,ak
The strength of our approach 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 .
#8094The 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.
tech,8-12-J05-1003,ak
Although the experiments in this article are on
<term>
natural language parsing ( NLP )
</term>
, the approach 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>
.
#8309Although the experiments in this article are onnatural 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.