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. |
tech,2-2-J05-1003,bq |
probabilistic parser
</term>
. The base
<term>
|
parser
|
</term>
produces a set of
<term>
candidate
|
#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,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,12-2-J05-1003,bq |
candidate parses
</term>
for each input
<term>
|
sentence
|
</term>
, with associated
<term>
probabilities
|
#8675
The base parser produces a set of candidate parses for each inputsentence, with associated probabilities that define an initial ranking of these parses. |
other,21-2-J05-1003,bq |
probabilities
</term>
that define an initial
<term>
|
ranking
|
</term>
of these
<term>
parses
</term>
. A second
|
#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,2-3-J05-1003,bq |
these
<term>
parses
</term>
. A second
<term>
|
model
|
</term>
then attempts to improve upon this
|
#8691
A secondmodel then attempts to improve upon this initial ranking, using additional features of the tree as evidence. |
other,10-3-J05-1003,bq |
attempts to improve upon this initial
<term>
|
ranking
|
</term>
, using additional
<term>
features
</term>
|
#8699
A second model then attempts to improve upon this initialranking, using additional features of the tree as evidence. |
other,17-3-J05-1003,bq |
additional
<term>
features
</term>
of the
<term>
|
tree
|
</term>
as evidence . The strength of our
|
#8706
A second model then attempts to improve upon this initial ranking, using additional features of thetree as evidence. |
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. |
other,10-4-J05-1003,bq |
approach
</term>
is that it allows a
<term>
|
tree
|
</term>
to be represented as an arbitrary
|
#8720
The 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,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,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. |
tech,4-5-J05-1003,bq |
</term>
into account . We introduce a new
<term>
|
method
|
</term>
for the
<term>
reranking task
</term>
|
#8763
We introduce a newmethod for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). |
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. |