#8085The 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,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,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.
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.
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,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.
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,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,23-9-J05-1003,ak
the
<term>
feature space
</term>
in the
<term>
parsing data
</term>
. Experiments show significant efficiency
#8249The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in theparsing data.
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,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.
tech,8-12-J05-1003,ak
experiments in this article are on
<term>
natural language parsing ( NLP )
</term>
, the approach should be applicable
#8309Although the experiments in this article are onnatural 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, or natural language generation.
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%.
tech,36-12-J05-1003,ak
ranking tasks
</term>
, for example ,
<term>
speech recognition
</term>
,
<term>
machine translation
</term>
#8337Although 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, or natural language generation.
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.
model,2-3-J05-1003,ak
these
<term>
parses
</term>
. A second
<term>
model
</term>
then attempts to improve upon this
#8056A secondmodel then attempts to improve upon this initial ranking, using additional features 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,16-9-J05-1003,ak
</term>
which takes advantage of the
<term>
sparsity
</term>
of the
<term>
feature space
</term>
in
#8242The article also introduces a new algorithm for the boosting approach which takes advantage of thesparsity of the feature space in the parsing data.
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.