| 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,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 desireddomain or genre. | |
| measure(ment),13-7-P03-1050,ak | indicates an improvement of 22-38 % in <term> | average precision | </term> over <term> unstemmed text </term> , | #4584 Task-based evaluation using Arabic information retrieval indicates an improvement of 22-38% inaverage precision over unstemmed text, and 96% of the performance of the proprietary stemmer above. | |
| tech,1-6-P03-1050,ak | needs <term> affix removal </term> . Our <term> | resource-frugal approach | </term> results in 87.5 % <term> agreement </term> | #4535 Ourresource-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. | |
| 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. | |
| 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. | |
| 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. | |
| measure(ment),7-6-P03-1050,ak | resource-frugal 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. |
