lr,1-3-P03-1050,bq |
<term>
training resources
</term>
. No
<term>
|
parallel text
|
</term>
is needed after the
<term>
training
|
#4477
Noparallel text is needed after the training phase. |
other,20-4-P03-1050,bq |
allowing it to adapt to a desired
<term>
|
domain
|
</term>
or
<term>
genre
</term>
. Examples and
|
#4506
Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desireddomain or genre. |
measure(ment),13-7-P03-1050,bq |
indicates an improvement of 22-38 % in
<term>
|
average precision
|
</term>
over
<term>
unstemmed text
</term>
,
|
#4582
Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% inaverage precision over unstemmed text, and 96% of the performance of the proprietary stemmer above. |
tech,1-6-P03-1050,bq |
needs
<term>
affix removal
</term>
. Our
<term>
|
resource-frugal approach
|
</term>
results in 87.5 %
<term>
agreement
</term>
|
#4533
Ourresource-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. |
other,16-5-P03-1050,bq |
the approach is applicable to any
<term>
|
language
|
</term>
that needs
<term>
affix removal
</term>
|
#4526
Examples and results will be given for Arabic, but the approach is applicable to anylanguage that needs affix removal. |
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. |
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. |
measure(ment),7-6-P03-1050,bq |
resource-frugal approach
</term>
results in 87.5 %
<term>
|
agreement
|
</term>
with a state of the art , proprietary
|
#4539
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. |