tech,6-9-J05-1003,bq |
The article also introduces a new
<term>
|
algorithm
|
</term>
for the
<term>
boosting approach
</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 |
significant efficiency gains for the new
<term>
|
algorithm
|
</term>
over the obvious
<term>
implementation
|
#8895
Experiments show significant efficiency gains for the newalgorithm over the obvious implementation of the boosting approach. |
tech,4-4-J05-1003,bq |
</term>
as evidence . The strength of our
<term>
|
approach
|
</term>
is that it allows a
<term>
tree
</term>
|
#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 |
language parsing ( NLP )
</term>
, the
<term>
|
approach
|
</term>
should be applicable to many other
|
#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 |
<term>
log-likelihood
</term>
under a
<term>
|
baseline model
|
</term>
( that of
<term>
Collins [ 1999 ]
</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 |
<term>
F-measure
</term>
error over the
<term>
|
baseline model ’s score
|
</term>
of 88.2 % . The article also introduces
|
#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,13-5-J05-1003,bq |
reranking task
</term>
, based on the
<term>
|
boosting approach
|
</term>
to
<term>
ranking problems
</term>
described
|
#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 |
a new
<term>
algorithm
</term>
for the
<term>
|
boosting approach
|
</term>
which takes advantage of the
<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,15-10-J05-1003,bq |
obvious
<term>
implementation
</term>
of the
<term>
|
boosting approach
|
</term>
. We argue that the method is an
|
#8902
Experiments show significant efficiency gains for the new algorithm over the obvious implementation of theboosting approach. |
tech,3-6-J05-1003,bq |
al. ( 1998 )
</term>
. We apply the
<term>
|
boosting method
|
</term>
to
<term>
parsing
</term>
the
<term>
Wall
|
#8789
We apply theboosting method to parsing the Wall Street Journal treebank. |
other,7-2-J05-1003,bq |
<term>
parser
</term>
produces a set of
<term>
|
candidate parses
|
</term>
for each input
<term>
sentence
</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 |
<term>
baseline model
</term>
( that of
<term>
|
Collins [ 1999 ]
|
</term>
) with evidence from an additional
|
#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 |
overlap and without the need to define a
<term>
|
derivation
|
</term>
or a
<term>
generative model
</term>
|
#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 |
simplicity and efficiency — to work on
<term>
|
feature selection methods
|
</term>
within
<term>
log-linear ( maximum-entropy
|
#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. |
measure(ment),6-8-J05-1003,bq |
<term>
model
</term>
achieved 89.75 %
<term>
|
F-measure
|
</term>
, a 13 % relative decrease in
<term>
|
#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%. |
measure(ment),14-8-J05-1003,bq |
</term>
, a 13 % relative decrease in
<term>
|
F-measure
|
</term>
error over the
<term>
baseline model
|
#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,20-5-J05-1003,bq |
ranking problems
</term>
described in
<term>
|
Freund et al. ( 1998 )
|
</term>
. We apply the
<term>
boosting method
|
#8779
We introduce a new method for the reranking task, based on the boosting approach to ranking problems described inFreund et al. ( 1998 ). |
tech,40-4-J05-1003,bq |
define a
<term>
derivation
</term>
or a
<term>
|
generative model
|
</term>
which takes these
<term>
features
</term>
|
#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,12-10-J05-1003,bq |
<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,4-7-J05-1003,bq |
The
<term>
method
</term>
combined the
<term>
|
log-likelihood
|
</term>
under a
<term>
baseline model
</term>
|
#8803
The method combined thelog-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. |