| 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. | |
| 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,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. | |
| other,7-3-P03-1050,ak | parallel text </term> is needed after the <term> | training phase | </term> . <term> Monolingual , unannotated | #4485 No parallel text is needed after thetraining phase. | |
| 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,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,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. | |
| 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. |
