#8025This article considers approaches which rerank the output of an existing probabilistic parser.
tech,1-2-J05-1003,ak
<term>
probabilistic parser
</term>
. The
<term>
base parser
</term>
produces a set of
<term>
candidate
#8029The base 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 of candidate 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 each input sentence, with associated probabilities that define an initial ranking of these parses.
other,16-2-J05-1003,ak
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 associated probabilities that define an initial ranking of these parses.
other,21-2-J05-1003,ak
probabilities
</term>
that define an initial
<term>
ranking
</term>
of these
<term>
parses
</term>
. A second
#8049The base parser produces a set of candidate parses for each input sentence, with associated probabilities 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 these parses .
model,2-3-J05-1003,ak
these
<term>
parses
</term>
. A second
<term>
model
</term>
then attempts to improve upon this
#8056A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence.
other,10-3-J05-1003,ak
attempts to improve upon this initial
<term>
ranking
</term>
, using additional
<term>
features
#8064A second model then attempts to improve upon this initial ranking , using additional features of the tree as evidence.
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 additional features of the tree as evidence.
other,17-3-J05-1003,ak
additional
<term>
features
</term>
of the
<term>
tree
</term>
as evidence . The strength of our
#8071A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence.
other,10-4-J05-1003,ak
our approach is that it allows a
<term>
tree
</term>
to be represented as an arbitrary
#8085The 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 these features into account.
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 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,26-4-J05-1003,ak
without concerns about how these
<term>
features
</term>
interact or overlap and without
#8101The 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 these features into account.
other,37-4-J05-1003,ak
and without the need to define a
<term>
derivation
</term>
or a
<term>
generative model
</term>
#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 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 a generative 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 these features 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 the reranking 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 the boosting 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 the boosting method to parsing the Wall Street Journal treebank.