| 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. | |
| 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. | |
| 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. | |
| 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. | |
| other,16-5-P03-1050,ak | the approach is applicable to any <term> | language | </term> that needs <term> affix removal </term> | #4528 Examples and results will be given for Arabic, but the approach is applicable to anylanguage that needs affix 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. | |
| lr,0-4-P03-1050,ak | after the <term> training phase </term> . <term> | Monolingual , unannotated text | </term> can be used to further improve the | #4488 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. | |
| 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,0-7-P03-1050,ak | <term> unsupervised component </term> . <term> | Task-based evaluation | </term> using <term> Arabic information retrieval | #4571 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,34-6-P03-1050,ak | annotated text </term> , in addition to an <term> | unsupervised component | </term> . <term> Task-based evaluation </term> | #4568 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. | |
| 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),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. | |
| 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. | |
| 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. | |
| 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,1-6-P03-1050,ak | needs <term> affix removal </term> . Our <term> | resource-frugal approach | </term> results in 87.5 % <term> agreement </term> | #4535 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,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. | |
| 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. | |
| 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,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. |
