#4499Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre.
other,20-4-P03-1050,ak
allowing it to adapt to a desired
<term>
domain
</term>
or
<term>
genre
</term>
. Examples and
#4508Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre.
other,22-4-P03-1050,ak
to a desired
<term>
domain
</term>
or
<term>
genre
</term>
. Examples and results will be given
#4510Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre .
other,16-5-P03-1050,ak
the approach is applicable to any
<term>
language
</term>
that needs
<term>
affix removal
</term>
#4528Examples and results will be given for Arabic, but the approach is applicable to any language that needs affix removal.
measure(ment),7-6-P03-1050,ak
approach
</term>
results in 87.5 %
<term>
agreement
</term>
with a state of the art , proprietary
#4541Our 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,28-7-P03-1050,ak
the performance of the proprietary
<term>
stemmer
</term>
above . We approximate
<term>
Arabic
#4599Task-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.