other,23-7-J05-1003,bq |
evidence from an additional 500,000
<term>
|
features
|
</term>
over
<term>
parse trees
</term>
that
|
#8822
The 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,23-9-J05-1003,bq |
of the feature space
</term>
in the
<term>
|
parsing data
|
</term>
. Experiments show significant efficiency
|
#8884
The 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,bq |
initial
<term>
ranking
</term>
of these
<term>
|
parses
|
</term>
. A second
<term>
model
</term>
then
|
#8687
The 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,bq |
additional 500,000
<term>
features
</term>
over
<term>
|
parse trees
|
</term>
that were not included in the original
|
#8824
The 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,bq |
, without concerns about how these
<term>
|
features
|
</term>
interact or overlap and without the
|
#8736
The 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,37-4-J05-1003,bq |
overlap and without the need to define a
<term>
|
derivation
|
</term>
or a
<term>
generative model
</term>
|
#8747
The 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,bq |
The
<term>
method
</term>
combined the
<term>
|
log-likelihood
|
</term>
under a
<term>
baseline model
</term>
|
#8803
The 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,bq |
generative model
</term>
which takes these
<term>
|
features
|
</term>
into account . We introduce a new
|
#8755
The 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,bq |
<term>
parser
</term>
produces a set of
<term>
|
candidate parses
|
</term>
for each input
<term>
sentence
</term>
|
#8670
The base parser produces a set ofcandidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. |
tech,1-7-J05-1003,bq |
Street Journal treebank
</term>
. The
<term>
|
method
|
</term>
combined the
<term>
log-likelihood
</term>
|
#8800
Themethod combined the log-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,bq |
which rerank the output of an existing
<term>
|
probabilistic parser
|
</term>
. The base
<term>
parser
</term>
produces
|
#8660
This article considers approaches which rerank the output of an existingprobabilistic parser. |
tech,13-5-J05-1003,bq |
reranking task
</term>
, based on the
<term>
|
boosting approach
|
</term>
to
<term>
ranking problems
</term>
described
|
#8772
We 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,bq |
obvious
<term>
implementation
</term>
of the
<term>
|
boosting approach
|
</term>
. We argue that the method is an
|
#8902
Experiments show significant efficiency gains for the new algorithm over the obvious implementation of theboosting approach. |
tech,16-12-J05-1003,bq |
language parsing ( NLP )
</term>
, the
<term>
|
approach
|
</term>
should be applicable to many other
|
#8952
Although the experiments in this article are on natural language parsing (NLP), theapproach 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,2-2-J05-1003,bq |
probabilistic parser
</term>
. The base
<term>
|
parser
|
</term>
produces a set of
<term>
candidate
|
#8665
The baseparser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. |
tech,2-3-J05-1003,bq |
these
<term>
parses
</term>
. A second
<term>
|
model
|
</term>
then attempts to improve upon this
|
#8691
A secondmodel then attempts to improve upon this initial ranking, using additional features of the tree as evidence. |
tech,2-8-J05-1003,bq |
original
<term>
model
</term>
. The new
<term>
|
model
|
</term>
achieved 89.75 %
<term>
F-measure
</term>
|
#8837
The newmodel achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%. |
tech,21-11-J05-1003,bq |
simplicity and efficiency — to work on
<term>
|
feature selection methods
|
</term>
within
<term>
log-linear ( maximum-entropy
|
#8926
We 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,25-11-J05-1003,bq |
feature selection methods
</term>
within
<term>
|
log-linear ( maximum-entropy ) models
|
</term>
. Although the experiments in this
|
#8930
We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work on feature selection methods withinlog-linear ( maximum-entropy ) models. |
tech,3-6-J05-1003,bq |
al. ( 1998 )
</term>
. We apply the
<term>
|
boosting method
|
</term>
to
<term>
parsing
</term>
the
<term>
Wall
|
#8789
We apply theboosting method to parsing the Wall Street Journal treebank. |