tech,11-1-J05-1003,bq |
which rerank the output of an existing
<term>
|
probabilistic parser
|
</term>
. The base
<term>
parser
</term>
produces
|
#8660
This article considers approaches which rerank the output of an existingprobabilistic parser. |
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,16-2-J05-1003,bq |
<term>
sentence
</term>
, with associated
<term>
|
probabilities
|
</term>
that define an initial
<term>
ranking
|
#8679
The base parser produces a set of candidate parses for each input sentence, with associatedprobabilities that define an initial ranking of these parses. |
other,24-2-J05-1003,bq |
initial
<term>
ranking
</term>
of these
<term>
|
parses
|
</term>
. A second
<term>
model
</term>
then
|
#8687
The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of theseparses. |
other,14-3-J05-1003,bq |
<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,19-4-J05-1003,bq |
represented as an arbitrary set of
<term>
|
features
|
</term>
, without concerns about how these
|
#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 |
, without concerns about how these
<term>
|
features
|
</term>
interact or overlap and without the
|
#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. |
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,45-4-J05-1003,bq |
generative model
</term>
which takes these
<term>
|
features
|
</term>
into account . We introduce a new
|
#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,7-5-J05-1003,bq |
introduce a new
<term>
method
</term>
for the
<term>
|
reranking task
|
</term>
, based on the
<term>
boosting approach
|
#8766
We introduce a new method for thereranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). |
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). |
other,16-5-J05-1003,bq |
the
<term>
boosting approach
</term>
to
<term>
|
ranking problems
|
</term>
described in
<term>
Freund et al. (
|
#8775
We introduce a new method for the reranking task, based on the boosting approach toranking problems described in Freund et al. (1998). |
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,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. |
tech,6-6-J05-1003,bq |
the
<term>
boosting method
</term>
to
<term>
|
parsing
|
</term>
the
<term>
Wall Street Journal treebank
|
#8792
We apply the boosting method toparsing the Wall Street Journal treebank. |
lr-prod,8-6-J05-1003,bq |
method
</term>
to
<term>
parsing
</term>
the
<term>
|
Wall Street Journal treebank
|
</term>
. The
<term>
method
</term>
combined
|
#8794
We apply the boosting method to parsing theWall Street Journal treebank. |
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. |
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,23-7-J05-1003,bq |
evidence from an additional 500,000
<term>
|
features
|
</term>
over
<term>
parse trees
</term>
that
|
#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. |
other,25-7-J05-1003,bq |
additional 500,000
<term>
features
</term>
over
<term>
|
parse trees
|
</term>
that were not included in the original
|
#8824
The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features overparse trees that were not included in the original model. |