| tech,16-6-P03-1050,ak | with a state of the art , proprietary <term> | Arabic stemmer | </term> built using <term> rules </term> , <term> | #4550 Our resource-frugal approach results in 87.5% agreement with a state of the art, proprietaryArabic stemmer built using rules, affix lists, and human annotated text, in addition to an unsupervised component. | |
| tech,19-5-P03-1050,ak | any <term> language </term> that needs <term> | affix removal | </term> . Our <term> resource-frugal approach | #4531 Examples and results will be given for Arabic, but the approach is applicable to any language that needsaffix removal. | |
| 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. | |
| 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. | |
| tech,34-6-P03-1050,ak | annotated text </term> , in addition to an <term> | unsupervised component | </term> . <term> Task-based evaluation </term> | #4568 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 anunsupervised component. | |
| 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,6-2-P03-1050,ak | <term> stemming model </term> is based on <term> | statistical machine translation | </term> and it uses an <term> English stemmer | #4454 The stemming model is based onstatistical machine translation and it uses an English stemmer and a small (10K sentences) parallel corpus as its sole training resources. |
