| 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,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. | |
| 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. | |
| 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. | |
| 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. | |
| 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,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. |
