#4461The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources.
tech,1-6-P03-1050,ak
needs
<term>
affix removal
</term>
. Our
<term>
resource-frugal approach
</term>
results in 87.5 %
<term>
agreement
#4535Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component.
tech,16-6-P03-1050,ak
a state of the art , proprietary
<term>
Arabic stemmer
</term>
built using
<term>
rules
</term>
,
<term>
#4550Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component.
tech,34-6-P03-1050,ak
annotated text
</term>
, in addition to an
<term>
unsupervised component
</term>
.
<term>
Task-based evaluation
</term>
#4568Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component .
tech,0-7-P03-1050,ak
<term>
unsupervised component
</term>
.
<term>
Task-based evaluation
</term>
using
<term>
Arabic information retrieval
#4571Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.
measure(ment),13-7-P03-1050,ak
indicates an improvement of 22-38 % in
<term>
average precision
</term>
over
<term>
unstemmed text
</term>
,
#4584Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text, and 96% of the performance of the proprietary stemmer above.
other,16-7-P03-1050,ak
<term>
average precision
</term>
over
<term>
unstemmed text
</term>
, and 96 % of the performance of
#4587Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision over unstemmed text , and 96% of the performance of the proprietary stemmer above.