measure(ment),0-5-H05-1012,bq |
<term>
machine translation tests
</term>
.
<term>
|
Performance
|
</term>
of the
<term>
algorithm
</term>
is contrasted
|
#7326
Significant improvement over traditional word alignment techniques is shown as well as improvement on several machine translation tests.Performance of the algorithm is contrasted with human annotation performance. |
tech,4-1-H05-1012,bq |
phrases
</term>
. This paper presents a
<term>
|
maximum entropy word alignment algorithm
|
</term>
for
<term>
Arabic-English
</term>
based
|
#7253
This paper presents amaximum entropy word alignment algorithm for Arabic-English based on supervised training data. |
lr,8-2-H05-1012,bq |
demonstrate that it is feasible to create
<term>
|
training material
|
</term>
for problems in
<term>
machine translation
|
#7274
We 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. |
other,10-1-H05-1012,bq |
word alignment algorithm
</term>
for
<term>
|
Arabic-English
|
</term>
based on
<term>
supervised training
|
#7259
This paper presents a maximum entropy word alignment algorithm forArabic-English based on supervised training data. |
tech,13-2-H05-1012,bq |
training material
</term>
for problems in
<term>
|
machine translation
|
</term>
and that a mixture of
<term>
supervised
|
#7279
We 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,bq |
translation
</term>
and that a mixture of
<term>
|
supervised and unsupervised methods
|
</term>
yields superior
<term>
performance
</term>
|
#7286
We 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. |
lr,13-1-H05-1012,bq |
<term>
Arabic-English
</term>
based on
<term>
|
supervised training data
|
</term>
. We demonstrate that it is feasible
|
#7262
This paper presents a maximum entropy word alignment algorithm for Arabic-English based onsupervised training data. |
measure(ment),15-4-H05-1012,bq |
as well as improvement on several
<term>
|
machine translation tests
|
</term>
.
<term>
Performance
</term>
of the
<term>
|
#7322
Significant improvement over traditional word alignment techniques is shown as well as improvement on severalmachine translation tests. |
measure(ment),26-2-H05-1012,bq |
unsupervised methods
</term>
yields superior
<term>
|
performance
|
</term>
. The
<term>
probabilistic model
</term>
|
#7292
We demonstrate that it is feasible to create training material for problems in machine translation and that a mixture of supervised and unsupervised methods yields superiorperformance. |
model,1-3-H05-1012,bq |
superior
<term>
performance
</term>
. The
<term>
|
probabilistic model
|
</term>
used in the
<term>
alignment
</term>
|
#7295
Theprobabilistic model used in the alignment directly models the link decisions. |
tech,6-3-H05-1012,bq |
probabilistic model
</term>
used in the
<term>
|
alignment
|
</term>
directly models the
<term>
link decisions
|
#7300
The probabilistic model used in thealignment directly models the link decisions. |
other,10-3-H05-1012,bq |
alignment
</term>
directly models the
<term>
|
link decisions
|
</term>
. Significant improvement over traditional
|
#7304
The probabilistic model used in the alignment directly models thelink decisions. |
tech,3-5-H05-1012,bq |
</term>
.
<term>
Performance
</term>
of the
<term>
|
algorithm
|
</term>
is contrasted with
<term>
human annotation
|
#7329
Performance of thealgorithm is contrasted with human annotation performance. |
measure(ment),7-5-H05-1012,bq |
algorithm
</term>
is contrasted with
<term>
|
human annotation performance
|
</term>
. This paper presents a
<term>
phrase-based
|
#7333
Performance of the algorithm is contrasted withhuman annotation performance. |