tech,4-1-P03-1050,ak users </term> . This paper presents an <term> unsupervised learning approach </term> to building a <term> non-English (
tech,10-1-P03-1050,ak learning approach </term> to building a <term> non-English ( Arabic ) stemmer </term> . The <term> stemming model </term> is
model,1-2-P03-1050,ak non-English ( Arabic ) stemmer </term> . The <term> stemming model </term> is based on <term> statistical machine
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
tech,13-2-P03-1050,ak machine translation </term> and it uses an <term> English stemmer </term> and a <term> small ( 10K sentences
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
lr,27-2-P03-1050,ak parallel corpus </term> as its sole <term> training resources </term> . No <term> parallel text </term> is
lr,1-3-P03-1050,ak <term> training resources </term> . No <term> parallel text </term> is needed after the <term> training
other,7-3-P03-1050,ak parallel text </term> is needed after the <term> training phase </term> . <term> Monolingual , unannotated
lr,0-4-P03-1050,ak after the <term> training phase </term> . <term> Monolingual , unannotated text </term> can be used to further improve the
tech,11-4-P03-1050,ak can be used to further improve the <term> stemmer </term> by allowing it to adapt to a desired
other,20-4-P03-1050,ak allowing it to adapt to a desired <term> domain </term> or <term> genre </term> . Examples and
other,22-4-P03-1050,ak to a desired <term> domain </term> or <term> genre </term> . Examples and results will be given
other,7-5-P03-1050,ak Examples and results will be given for <term> Arabic </term> , but the approach is applicable
other,16-5-P03-1050,ak the approach is applicable to any <term> language </term> that needs <term> affix removal </term>
tech,19-5-P03-1050,ak any <term> language </term> that needs <term> affix removal </term> . Our <term> resource-frugal approach
tech,1-6-P03-1050,ak needs <term> affix removal </term> . Our <term> resource-frugal approach </term> results in 87.5 % <term> agreement </term>
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
tech,16-6-P03-1050,ak with a state of the art , proprietary <term> Arabic stemmer </term> built using <term> rules </term> , <term>
model,20-6-P03-1050,ak <term> Arabic stemmer </term> built using <term> rules </term> , <term> affix lists </term> , and <term>
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