| tech,11-4-P03-1050,ak | <term> Monolingual , unannotated text </term> can be used to further improve the <term> stemmer </term> by allowing it to adapt to a desired <term> domain </term> or <term> genre </term> . | #4499 Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. | |
| other,20-4-P03-1050,ak | <term> Monolingual , unannotated text </term> can be used to further improve the <term> stemmer </term> by allowing it to adapt to a desired <term> domain </term> or <term> genre </term> . | #4508 Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre. | |
| other,22-4-P03-1050,ak | <term> Monolingual , unannotated text </term> can be used to further improve the <term> stemmer </term> by allowing it to adapt to a desired <term> domain </term> or <term> genre </term> . | #4510 Monolingual, unannotated text can be used to further improve the stemmer by allowing it to adapt to a desired domain or genre . | |
| other,16-5-P03-1050,ak | Examples and results will be given for <term> Arabic </term> , but 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 any language that needs affix removal. | |
| measure(ment),7-6-P03-1050,ak | Our <term> resource-frugal approach </term> results in 87.5 % <term> agreement </term> with a state of the art , proprietary <term> Arabic stemmer </term> built using <term> rules </term> , <term> affix lists </term> , and <term> human annotated text </term> , in addition to an <term> unsupervised component </term> . | #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. | |
| tech,28-7-P03-1050,ak | <term> Task-based evaluation </term> using <term> Arabic information retrieval </term> indicates an improvement of 22-38 % in <term> average precision </term> over <term> unstemmed text </term> , and 96 % of the performance of the proprietary <term> stemmer </term> above . | #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 proprietary stemmer above. |
