| tech,4-1-P03-1050,bq | This paper presents an <term> unsupervised learning approach </term> to building a <term> non-English ( Arabic ) stemmer </term> . | #4434 This paper presents anunsupervised learning approach to building a non-English (Arabic) stemmer. |
| tech,10-1-P03-1050,bq | This paper presents an <term> unsupervised learning approach </term> to building a <term> non-English ( Arabic ) stemmer </term> . | #4440 This paper presents an unsupervised learning approach to building anon-English ( Arabic ) stemmer. |
| model,1-2-P03-1050,bq | The <term> stemming model </term> is based on <term> statistical machine translation </term> and it uses an <term> English stemmer </term> and a small ( 10K sentences ) <term> parallel corpus </term> as its sole <term> training resources </term> . | #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,6-2-P03-1050,bq | The <term> stemming model </term> is based on <term> statistical machine translation </term> and it uses an <term> English stemmer </term> and a small ( 10K sentences ) <term> parallel corpus </term> as its sole <term> training resources </term> . | #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,13-2-P03-1050,bq | The <term> stemming model </term> is based on <term> statistical machine translation </term> and it uses an <term> English stemmer </term> and a small ( 10K sentences ) <term> parallel corpus </term> as its sole <term> training resources </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. |
| lr,22-2-P03-1050,bq | The <term> stemming model </term> is based on <term> statistical machine translation </term> and it uses an <term> English stemmer </term> and a small ( 10K sentences ) <term> parallel corpus </term> as its sole <term> training resources </term> . | #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,27-2-P03-1050,bq | The <term> stemming model </term> is based on <term> statistical machine translation </term> and it uses an <term> English stemmer </term> and a small ( 10K sentences ) <term> parallel corpus </term> as its sole <term> training resources </term> . | #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. |
| lr,1-3-P03-1050,bq | No <term> parallel text </term> is needed after the <term> training phase </term> . | #4477 Noparallel text is needed after the training phase. |
| other,7-3-P03-1050,bq | No <term> parallel text </term> is needed after the <term> training phase </term> . | #4483 No parallel text is needed after thetraining phase. |
| lr,0-4-P03-1050,bq | No <term> parallel text </term> is needed after the <term> training phase </term> . <term> Monolingual , unannotated text </term> can be used to further improve the <term> stemmer </term> by allowing it to adapt to a desired <term> domain </term> or <term> genre </term> . | #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,11-4-P03-1050,bq | <term> Monolingual , unannotated text </term> can be used to further improve the <term> stemmer </term> by allowing it to adapt to a desired <term> domain </term> or <term> genre </term> . | #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 | <term> Monolingual , unannotated text </term> can be used to further improve the <term> stemmer </term> by allowing it to adapt to a desired <term> domain </term> or <term> genre </term> . | #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 | <term> Monolingual , unannotated text </term> can be used to further improve the <term> stemmer </term> by allowing it to adapt to a desired <term> domain </term> or <term> genre </term> . | #4508 Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain orgenre. |
| other,7-5-P03-1050,bq | Examples and results will be given for <term> Arabic </term> , but the approach is applicable to any <term> language </term> that needs <term> affix removal </term> . | #4517 Examples and results will be given forArabic, but the approach is applicable to any language that needs affix removal. |
| other,16-5-P03-1050,bq | Examples and results will be given for <term> Arabic </term> , but 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,19-5-P03-1050,bq | Examples and results will be given for <term> Arabic </term> , but the approach is applicable to any <term> language </term> that needs <term> affix removal </term> . | #4529 Examples and results will be given for Arabic, but the approach is applicable to any language that needsaffix removal. |
| tech,1-6-P03-1050,bq | Our <term> resource-frugal approach </term> results in 87.5 % <term> agreement </term> with a state of the art , proprietary <term> Arabic stemmer </term> built using <term> rules </term> , <term> affix lists </term> , and <term> human annotated text </term> , in addition to an <term> unsupervised component </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 | Our <term> resource-frugal approach </term> results in 87.5 % <term> agreement </term> with a state of the art , proprietary <term> Arabic stemmer </term> built using <term> rules </term> , <term> affix lists </term> , and <term> human annotated text </term> , in addition to an <term> unsupervised component </term> . | #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,16-6-P03-1050,bq | Our <term> resource-frugal approach </term> results in 87.5 % <term> agreement </term> with a state of the art , proprietary <term> Arabic stemmer </term> built using <term> rules </term> , <term> affix lists </term> , and <term> human annotated text </term> , in addition to an <term> unsupervised component </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. |
| model,20-6-P03-1050,bq | Our <term> resource-frugal approach </term> results in 87.5 % <term> agreement </term> with a state of the art , proprietary <term> Arabic stemmer </term> built using <term> rules </term> , <term> affix lists </term> , and <term> human annotated text </term> , in addition to an <term> unsupervised component </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. |
