other,2-1-P03-1051,bq stemmer </term> above . We approximate <term> Arabic 's rich morphology </term> by a <term> model </term> that a <term>
model,8-1-P03-1051,bq Arabic 's rich morphology </term> by a <term> model </term> that a <term> word </term> consists of
other,11-1-P03-1051,bq </term> by a <term> model </term> that a <term> word </term> consists of a sequence of <term> morphemes
other,17-1-P03-1051,bq word </term> consists of a sequence of <term> morphemes </term> in the <term> pattern </term><term> prefix
other,20-1-P03-1051,bq sequence of <term> morphemes </term> in the <term> pattern </term><term> prefix * - stem-suffix * </term>
other,21-1-P03-1051,bq morphemes </term> in the <term> pattern </term><term> prefix * - stem-suffix * </term> ( * denotes zero or more occurrences
other,35-1-P03-1051,bq denotes zero or more occurrences of a <term> morpheme </term> ) . Our method is seeded by a small
lr,7-2-P03-1051,bq . Our method is seeded by a small <term> manually segmented Arabic corpus </term> and uses it to bootstrap an <term>
tech,17-2-P03-1051,bq </term> and uses it to bootstrap an <term> unsupervised algorithm </term> to build the <term> Arabic word segmenter
tech,22-2-P03-1051,bq unsupervised algorithm </term> to build the <term> Arabic word segmenter </term> from a large <term> unsegmented Arabic
lr,28-2-P03-1051,bq word segmenter </term> from a large <term> unsegmented Arabic corpus </term> . The <term> algorithm </term> uses a
tech,1-3-P03-1051,bq unsegmented Arabic corpus </term> . The <term> algorithm </term> uses a <term> trigram language model
model,4-3-P03-1051,bq </term> . The <term> algorithm </term> uses a <term> trigram language model </term> to determine the most probable <term>
other,12-3-P03-1051,bq </term> to determine the most probable <term> morpheme sequence </term> for a given <term> input </term> . The
other,17-3-P03-1051,bq morpheme sequence </term> for a given <term> input </term> . The <term> language model </term> is
model,1-4-P03-1051,bq for a given <term> input </term> . The <term> language model </term> is initially estimated from a small
lr,9-4-P03-1051,bq is initially estimated from a small <term> manually segmented corpus </term> of about 110,000 <term> words </term>
other,15-4-P03-1051,bq segmented corpus </term> of about 110,000 <term> words </term> . To improve the <term> segmentation
tech,3-5-P03-1051,bq <term> words </term> . To improve the <term> segmentation </term><term> accuracy </term> , we use an <term>
measure(ment),4-5-P03-1051,bq improve the <term> segmentation </term><term> accuracy </term> , we use an <term> unsupervised algorithm
skrij podrobnosti