#8056A secondmodel then attempts to improve upon this initial ranking, using additional features of the tree as evidence.
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,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 thetree as evidence.
other,30-12-J05-1003,ak
</term>
which are naturally framed as
<term>
ranking tasks
</term>
, for example ,
<term>
speech recognition
#8331Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed asranking tasks, for example, speech recognition, machine translation, or natural language generation.
tech,23-12-J05-1003,ak
should be applicable to many other
<term>
NLP problems
</term>
which are naturally framed as
<term>
#8324Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many otherNLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation.
other,19-9-J05-1003,ak
of the
<term>
sparsity
</term>
of the
<term>
feature space
</term>
in the
<term>
parsing data
</term>
.
#8245The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of thefeature space in the parsing data.
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.
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.
tech,11-1-J05-1003,ak
which rerank the output of an existing
<term>
probabilistic parser
</term>
. The
<term>
base parser
</term>
produces
#8025This article considers approaches which rerank the output of an existingprobabilistic parser.
tech,15-10-J05-1003,ak
the obvious implementation of the
<term>
boosting approach
</term>
. We argue that the method is an
#8267Experiments show significant efficiency gains for the new algorithm over the obvious implementation of theboosting approach.
tech,43-12-J05-1003,ak
<term>
machine translation
</term>
, or
<term>
natural language generation
</term>
. We present a novel method for discovering
#8344Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, ornatural language generation.
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.
measure(ment),6-8-J05-1003,ak
<term>
model
</term>
achieved 89.75 %
<term>
F-measure
</term>
, a 13 % relative decrease in
<term>
#8206The new model achieved 89.75%F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%.
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.
tech,8-10-J05-1003,ak
significant efficiency gains for the new
<term>
algorithm
</term>
over the obvious implementation of
#8260Experiments show significant efficiency gains for the newalgorithm over the obvious implementation of the boosting approach.
other,37-4-J05-1003,ak
overlap 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 aderivation or a generative model which takes these features into account.
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 initialranking of these parses.
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,6-9-J05-1003,ak
The article also introduces a new
<term>
algorithm
</term>
for the
<term>
boosting approach
</term>
#8232The article also introduces a newalgorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data.