tech,13-2-P03-1050,bq |
machine translation
</term>
and it uses an
<term>
|
English stemmer
|
</term>
and a small ( 10K sentences )
<term>
|
#4459
The stemming model is based on statistical machine translation and it uses anEnglish stemmer and a small (10K sentences) parallel corpus as its sole training resources. |
tech,11-4-P03-1050,bq |
can be used to further improve the
<term>
|
stemmer
|
</term>
by allowing it to adapt to a desired
|
#4497
Monolingual, unannotated text can be used to further improve thestemmer by allowing it to adapt to a desired domain or genre. |
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. |
tech,19-5-P03-1050,bq |
any
<term>
language
</term>
that needs
<term>
|
affix removal
|
</term>
. Our
<term>
resource-frugal approach
|
#4529
Examples and results will be given for Arabic, but the approach is applicable to any language that needsaffix removal. |
tech,3-7-P03-1050,bq |
<term>
Task-based evaluation
</term>
using
<term>
|
Arabic information retrieval
|
</term>
indicates an improvement of 22-38
|
#4572
Task-based evaluation usingArabic information retrieval indicates an improvement of 22-38% in average 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. |
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. |
tech,10-1-P03-1050,bq |
learning approach
</term>
to building a
<term>
|
non-English ( Arabic ) stemmer
|
</term>
. The
<term>
stemming model
</term>
is
|
#4440
This paper presents an unsupervised learning approach to building anon-English ( Arabic ) stemmer. |
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. |
lr,0-4-P03-1050,bq |
after the
<term>
training phase
</term>
.
<term>
|
Monolingual , unannotated text
|
</term>
can be used to further improve the
|
#4486
No parallel text is needed after the training phase.Monolingual , unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. |
tech,6-2-P03-1050,bq |
<term>
stemming model
</term>
is based on
<term>
|
statistical machine translation
|
</term>
and it uses an
<term>
English stemmer
|
#4452
The stemming model is based onstatistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. |
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. |
model,20-6-P03-1050,bq |
<term>
Arabic stemmer
</term>
built using
<term>
|
rules
|
</term>
,
<term>
affix lists
</term>
, and
<term>
|
#4552
Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built usingrules, affix lists, and human annotated text, in addition to an unsupervised component. |
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,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),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,16-6-P03-1050,bq |
with a state of the art , proprietary
<term>
|
Arabic stemmer
|
</term>
built using
<term>
rules
</term>
,
<term>
|
#4548
Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietaryArabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. |
other,7-5-P03-1050,bq |
Examples and results will be given for
<term>
|
Arabic
|
</term>
, but the approach is applicable
|
#4517
Examples and results will be given forArabic, but the approach is applicable to any language that needs affix removal. |
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. |