#8025This article considers approaches which rerank the output of an existingprobabilistic parser.
tech,1-2-J05-1003,ak
<term>
probabilistic parser
</term>
. The
<term>
base parser
</term>
produces a set of
<term>
candidate
#8029Thebase parser 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,ak
base parser
</term>
produces a set of
<term>
candidate parses
</term>
for each
<term>
input sentence
</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,11-2-J05-1003,ak
<term>
candidate parses
</term>
for each
<term>
input sentence
</term>
, with associated
<term>
probabilities
#8039The base parser produces a set of candidate parses for eachinput sentence, with associated probabilities that define an initial ranking of these parses.
other,16-2-J05-1003,ak
input sentence
</term>
, with associated
<term>
probabilities
</term>
that define an initial
<term>
ranking
#8044The 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,ak
initial
<term>
ranking
</term>
of these
<term>
parses
</term>
. A second
<term>
model
</term>
then
#8052The 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,ak
<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,19-4-J05-1003,ak
represented as an arbitrary set of
<term>
features
</term>
, without concerns about how these
#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.
other,26-4-J05-1003,ak
, without concerns about how these
<term>
features
</term>
interact or overlap and without the
#8101The 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.
model,40-4-J05-1003,ak
define a
<term>
derivation
</term>
or a
<term>
generative model
</term>
which takes these
<term>
features
</term>
#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,45-4-J05-1003,ak
generative model
</term>
which takes these
<term>
features
</term>
into account . We introduce a new
#8120The 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.
other,7-5-J05-1003,ak
We introduce a new method for the
<term>
reranking task
</term>
, based on the
<term>
boosting approach
#8131We 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,ak
reranking task
</term>
, based on the
<term>
boosting approach to ranking problems
</term>
described in Freund et al. ( 1998
#8137We introduce a new method for the reranking task, based on theboosting approach to ranking problems described in Freund et al. (1998).
tech,3-6-J05-1003,ak
Freund et al. ( 1998 ) . We apply the
<term>
boosting method
</term>
to parsing the
<term>
Wall Street Journal
#8154We apply theboosting method to parsing the Wall Street Journal treebank.
lr,8-6-J05-1003,ak
boosting method
</term>
to parsing the
<term>
Wall Street Journal treebank
</term>
. The method combined the
<term>
log-likelihood
#8159We apply the boosting method to parsing theWall Street Journal treebank.
other,4-7-J05-1003,ak
treebank
</term>
. The method combined the
<term>
log-likelihood under a baseline model
</term>
( that of Collins [ 1999 ] ) with
#8168The 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.
other,23-7-J05-1003,ak
evidence from an additional 500,000
<term>
features
</term>
over
<term>
parse trees
</term>
that
#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,25-7-J05-1003,ak
additional 500,000
<term>
features
</term>
over
<term>
parse trees
</term>
that were not included in the original
#8189The 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.
measure(ment),14-8-J05-1003,ak
</term>
, a 13 % relative decrease in
<term>
F-measure error
</term>
over the
<term>
baseline model ’s
</term>
#8214The new model achieved 89.75% F-measure, a 13% relative decrease inF-measure error over the baseline model’s score of 88.2%.
model,18-8-J05-1003,ak
<term>
F-measure error
</term>
over the
<term>
baseline model ’s
</term>
score of 88.2 % . The article also
#8218The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over thebaseline model ’s score of 88.2%.