tech,28-7-P03-1050,bq |
the performance of the proprietary
<term>
|
stemmer
|
</term>
above . We approximate
<term>
Arabic
|
#4597
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 proprietarystemmer above. |
model,1-2-P03-1050,bq |
non-English ( Arabic ) stemmer
</term>
. The
<term>
|
stemming model
|
</term>
is based on
<term>
statistical machine
|
#4447
Thestemming 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,0-7-P03-1050,bq |
<term>
unsupervised component
</term>
.
<term>
|
Task-based evaluation
|
</term>
using
<term>
Arabic information retrieval
|
#4569
Our 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. |
other,7-3-P03-1050,bq |
parallel text
</term>
is needed after the
<term>
|
training phase
|
</term>
.
<term>
Monolingual , unannotated
|
#4483
No parallel text is needed after thetraining phase. |
lr,27-2-P03-1050,bq |
<term>
parallel corpus
</term>
as its sole
<term>
|
training resources
|
</term>
. No
<term>
parallel text
</term>
is
|
#4473
The stemming model is based on statistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its soletraining resources. |
other,16-7-P03-1050,bq |
<term>
average precision
</term>
over
<term>
|
unstemmed text
|
</term>
, and 96 % of the performance of
|
#4585
Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% in average precision overunstemmed text, and 96% of the performance of the proprietary stemmer above. |
tech,34-6-P03-1050,bq |
annotated text
</term>
, in addition to an
<term>
|
unsupervised component
|
</term>
.
<term>
Task-based evaluation
</term>
|
#4566
Our 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 anunsupervised component. |
tech,4-1-P03-1050,bq |
users
</term>
. This paper presents an
<term>
|
unsupervised learning approach
|
</term>
to building a
<term>
non-English (
|
#4434
This paper presents anunsupervised learning approach to building a non-English (Arabic) stemmer. |