| lr,17-2-P03-1050,ak | an <term> English stemmer </term> and a <term> | small ( 10K sentences ) parallel corpus | </term> as its sole <term> training resources | #4465 The stemming model is based on statistical machine translation and it uses an English stemmer and asmall ( 10K sentences ) parallel corpus as its sole training resources. | |
| 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. | |
| lr,1-3-P03-1050,ak | <term> training resources </term> . No <term> | parallel text | </term> is needed after the <term> training | #4479 Noparallel text is needed after the training phase. | |
| 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,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. | |
| 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,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. |
