#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,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,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,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.
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.
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,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,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,7-2-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,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,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.
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,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,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,21-11-J05-1003,ak
simplicity and efficiency — to work on
<term>
feature selection methods
</term>
within
<term>
log-linear ( maximum-entropy
#8291We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work onfeature selection methods within log-linear (maximum-entropy) models.
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.
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.
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.
tech,39-12-J05-1003,ak
,
<term>
speech recognition
</term>
,
<term>
machine translation
</term>
, or
<term>
natural language generation
#8340Although 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.
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.