| 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 the stemmer by allowing it to adapt to a desired domain or genre. | |
| other,20-4-P03-1050,ak | allowing it to adapt to a desired <term> | domain | </term> or <term> genre </term> . Examples and | #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 | 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 or genre . | |
| 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 any language that needs affix removal. | |
| measure(ment),7-6-P03-1050,ak | approach </term> results in 87.5 % <term> | agreement | </term> with a state of the art , proprietary | #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 | 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 proprietary stemmer above. |
