This paper presents a
<term>
maximum entropy word alignment algorithm
</term>
for
<term>
Arabic-English
</term>
based on
<term>
supervised training data
</term>
.
#5279This paper presents amaximum entropy word alignment algorithm for Arabic-English based on supervised training data.
other,10-1-H05-1012,ak
This paper presents a
<term>
maximum entropy word alignment algorithm
</term>
for
<term>
Arabic-English
</term>
based on
<term>
supervised training data
</term>
.
#5285This paper presents a maximum entropy word alignment algorithm forArabic-English based on supervised training data.
lr,13-1-H05-1012,ak
This paper presents a
<term>
maximum entropy word alignment algorithm
</term>
for
<term>
Arabic-English
</term>
based on
<term>
supervised training data
</term>
.
#5288This paper presents a maximum entropy word alignment algorithm for Arabic-English based onsupervised training data.
lr,8-2-H05-1012,ak
We demonstrate that it is feasible to create
<term>
training material
</term>
for problems in
<term>
machine translation
</term>
and that a mixture of
<term>
supervised and unsupervised methods
</term>
yields superior performance .
#5300We demonstrate that it is feasible to createtraining material for problems in machine translation and that a mixture of supervised and unsupervised methods yields superior performance.
tech,13-2-H05-1012,ak
We demonstrate that it is feasible to create
<term>
training material
</term>
for problems in
<term>
machine translation
</term>
and that a mixture of
<term>
supervised and unsupervised methods
</term>
yields superior performance .
#5305We demonstrate that it is feasible to create training material for problems inmachine translation and that a mixture of supervised and unsupervised methods yields superior performance.
tech,20-2-H05-1012,ak
We demonstrate that it is feasible to create
<term>
training material
</term>
for problems in
<term>
machine translation
</term>
and that a mixture of
<term>
supervised and unsupervised methods
</term>
yields superior performance .
#5312We demonstrate that it is feasible to create training material for problems in machine translation and that a mixture ofsupervised and unsupervised methods yields superior performance.
model,1-3-H05-1012,ak
The
<term>
probabilistic model
</term>
used in the
<term>
alignment
</term>
directly models the link decisions .
#5321Theprobabilistic model used in the alignment directly models the link decisions.
tech,6-3-H05-1012,ak
The
<term>
probabilistic model
</term>
used in the
<term>
alignment
</term>
directly models the link decisions .
#5326The probabilistic model used in thealignment directly models the link decisions.
tech,3-4-H05-1012,ak
Significant improvement over
<term>
traditional word alignment techniques
</term>
is shown as well as improvement on several
<term>
machine translation tests
</term>
.
#5336Significant improvement overtraditional word alignment techniques is shown as well as improvement on several machine translation tests.
measure(ment),15-4-H05-1012,ak
Significant improvement over
<term>
traditional word alignment techniques
</term>
is shown as well as improvement on several
<term>
machine translation tests
</term>
.
#5348Significant improvement over traditional word alignment techniques is shown as well as improvement on severalmachine translation tests.
tech,3-5-H05-1012,ak
Performance of the
<term>
algorithm
</term>
is contrasted with
<term>
human annotation performance
</term>
.
#5355Performance of thealgorithm is contrasted with human annotation performance.
measure(ment),7-5-H05-1012,ak
Performance of the
<term>
algorithm
</term>
is contrasted with
<term>
human annotation performance
</term>
.
#5359Performance of the algorithm is contrasted withhuman annotation performance.