tech,4-1-H05-1012,bq |
This paper presents a
<term>
maximum entropy word alignment algorithm
</term>
for
<term>
Arabic-English
</term>
based on
<term>
supervised training data
</term>
.
|
#7253
This paper presents amaximum entropy word alignment algorithm for Arabic-English based on supervised training data. |
other,10-1-H05-1012,bq |
This paper presents a
<term>
maximum entropy word alignment algorithm
</term>
for
<term>
Arabic-English
</term>
based on
<term>
supervised training data
</term>
.
|
#7259
This paper presents a maximum entropy word alignment algorithm forArabic-English based on supervised training data. |
lr,13-1-H05-1012,bq |
This paper presents a
<term>
maximum entropy word alignment algorithm
</term>
for
<term>
Arabic-English
</term>
based on
<term>
supervised training data
</term>
.
|
#7262
This paper presents a maximum entropy word alignment algorithm for Arabic-English based onsupervised training data. |
lr,8-2-H05-1012,bq |
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
<term>
performance
</term>
.
|
#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. |
tech,13-2-H05-1012,bq |
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
<term>
performance
</term>
.
|
#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. |
measure(ment),26-2-H05-1012,bq |
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
<term>
performance
</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 |
The
<term>
probabilistic model
</term>
used in the
<term>
alignment
</term>
directly models the
<term>
link decisions
</term>
.
|
#7295
Theprobabilistic model used in the alignment directly models the link decisions. |
tech,6-3-H05-1012,bq |
The
<term>
probabilistic model
</term>
used in the
<term>
alignment
</term>
directly models the
<term>
link decisions
</term>
.
|
#7300
The probabilistic model used in thealignment directly models the link decisions. |
other,10-3-H05-1012,bq |
The
<term>
probabilistic model
</term>
used in the
<term>
alignment
</term>
directly models the
<term>
link decisions
</term>
.
|
#7304
The probabilistic model used in the alignment directly models thelink decisions. |
tech,4-4-H05-1012,bq |
Significant improvement over traditional
<term>
word alignment techniques
</term>
is shown as well as improvement on several
<term>
machine translation tests
</term>
.
|
#7311
Significant improvement over traditionalword alignment techniques is shown as well as improvement on several machine translation tests. |
measure(ment),15-4-H05-1012,bq |
Significant improvement over traditional
<term>
word alignment techniques
</term>
is shown as well as improvement on several
<term>
machine translation tests
</term>
.
|
#7322
Significant improvement over traditional word alignment techniques is shown as well as improvement on severalmachine translation tests. |
measure(ment),0-5-H05-1012,bq |
Significant improvement over traditional
<term>
word alignment techniques
</term>
is shown as well as improvement on several
<term>
machine translation tests
</term>
.
<term>
Performance
</term>
of the
<term>
algorithm
</term>
is contrasted with
<term>
human annotation performance
</term>
.
|
#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,3-5-H05-1012,bq |
<term>
Performance
</term>
of the
<term>
algorithm
</term>
is contrasted with
<term>
human annotation performance
</term>
.
|
#7329
Performance of thealgorithm is contrasted with human annotation performance. |
measure(ment),7-5-H05-1012,bq |
<term>
Performance
</term>
of the
<term>
algorithm
</term>
is contrasted with
<term>
human annotation performance
</term>
.
|
#7333
Performance of the algorithm is contrasted withhuman annotation performance. |