| tech,13-2-P03-1050,ak | machine translation </term> and it uses an <term> | English stemmer | </term> and a <term> small ( 10K sentences | #4461 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. | |
| other,22-4-P03-1050,ak | to a desired <term> domain </term> or <term> | genre | </term> . Examples and results will be given | #4510 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,ak | Examples and results will be given for <term> | Arabic | </term> , but the approach is applicable | #4519 Examples and results will be given forArabic, but the approach is applicable to any language that needs affix removal. | |
| measure(ment),7-6-P03-1050,ak | resource-frugal approach </term> results in 87.5 % <term> | agreement | </term> with a state of the art , proprietary | #4541 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. | |
| other,16-7-P03-1050,ak | <term> average precision </term> over <term> | unstemmed text | </term> , and 96 % of the performance of | #4587 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. | |
| tech,16-6-P03-1050,ak | with a state of the art , proprietary <term> | Arabic stemmer | </term> built using <term> rules </term> , <term> | #4550 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,ak | <term> Arabic stemmer </term> built using <term> | rules | </term> , <term> affix lists </term> , and <term> | #4554 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. | |
| tech,10-1-P03-1050,ak | learning approach </term> to building a <term> | non-English ( Arabic ) stemmer | </term> . The <term> stemming model </term> is | #4442 This paper presents an unsupervised learning approach to building anon-English ( Arabic ) stemmer. | |
| lr,27-2-P03-1050,ak | parallel corpus </term> as its sole <term> | training resources | </term> . No <term> parallel text </term> is | #4475 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. | |
| other,20-4-P03-1050,ak | allowing it to adapt to a desired <term> | domain | </term> or <term> genre </term> . Examples and | #4508 Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desireddomain or genre. | |
| tech,6-2-P03-1050,ak | <term> stemming model </term> is based on <term> | statistical machine translation | </term> and it uses an <term> English stemmer | #4454 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,3-7-P03-1050,ak | <term> Task-based evaluation </term> using <term> | Arabic information retrieval | </term> indicates an improvement of 22-38 | #4574 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. | |
| lr,26-6-P03-1050,ak | </term> , <term> affix lists </term> , and <term> | human annotated text | </term> , in addition to an <term> unsupervised | #4560 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. | |
| model,1-2-P03-1050,ak | non-English ( Arabic ) stemmer </term> . The <term> | stemming model | </term> is based on <term> statistical machine | #4449 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,ak | users </term> . This paper presents an <term> | unsupervised learning approach | </term> to building a <term> non-English ( | #4436 This paper presents anunsupervised learning approach to building a non-English (Arabic) stemmer. | |
| measure(ment),13-7-P03-1050,ak | indicates an improvement of 22-38 % in <term> | average precision | </term> over <term> unstemmed text </term> , | #4584 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,28-7-P03-1050,ak | the performance of the proprietary <term> | stemmer | </term> above . We approximate <term> Arabic | #4599 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 proprietarystemmer above. | |
| lr,22-6-P03-1050,ak | </term> built using <term> rules </term> , <term> | affix lists | </term> , and <term> human annotated text </term> | #4556 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,19-5-P03-1050,ak | any <term> language </term> that needs <term> | affix removal | </term> . Our <term> resource-frugal approach | #4531 Examples and results will be given for Arabic, but the approach is applicable to any language that needsaffix removal. | |
| tech,11-4-P03-1050,ak | can be used to further improve the <term> | stemmer | </term> by allowing it to adapt to a desired | #4499 Monolingual, unannotated text can be used to further improve thestemmer by allowing it to adapt to a desired domain or genre. |
