Concordance

Query P03-1050 28 >
Multilevel Sort 28 (1,305.4 per million)

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.
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.
lr,22-2-P03-1050,bq	</term> and a small ( 10K sentences ) <term>	parallel corpus	</term> as its sole <term> training resources	#4468 The 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.
lr,22-6-P03-1050,bq	</term> built using <term> rules </term> , <term>	affix lists	</term> , and <term> human annotated text </term>	#4554 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.
lr,26-6-P03-1050,bq	</term> , <term> affix lists </term> , and <term>	human annotated text	</term> , in addition to an <term> unsupervised	#4558 Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietary Arabic stemmer built using rules, affix lists, andhuman annotated text, in addition to an unsupervised component.
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.
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.
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.
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.
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.
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.
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.
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.
other,22-4-P03-1050,bq	to a desired <term> domain </term> or <term>	genre	</term> . Examples and results will be given	#4508 Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain orgenre.
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.
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.
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.
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.
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.
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.


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