| other,2-1-P03-1051,ak | stemmer </term> above . We approximate <term> | Arabic 's rich morphology | </term> by a model that a <term> word </term> | #4604 We approximateArabic 's rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). | |
| other,11-1-P03-1051,ak | morphology </term> by a model that a <term> | word | </term> consists of a sequence of <term> morphemes | #4613 We approximate Arabic's rich morphology by a model that aword consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). | |
| other,17-1-P03-1051,ak | word </term> consists of a sequence of <term> | morphemes | </term> in the <term> pattern </term> prefix | #4619 We approximate Arabic's rich morphology by a model that a word consists of a sequence ofmorphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). | |
| other,20-1-P03-1051,ak | sequence of <term> morphemes </term> in the <term> | pattern | </term> prefix * - stem-suffix * ( * denotes | #4622 We approximate Arabic's rich morphology by a model that a word consists of a sequence of morphemes in thepattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). | |
| other,35-1-P03-1051,ak | denotes zero or more occurrences of a <term> | morpheme | </term> ) . Our method is seeded by a <term> | #4637 We approximate Arabic's rich morphology by a model that a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of amorpheme). | |
| lr,6-2-P03-1051,ak | </term> ) . Our method is seeded by a <term> | small manually segmented Arabic corpus | </term> and uses it to bootstrap an <term> | #4646 Our method is seeded by asmall manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus. | |
| tech,17-2-P03-1051,ak | </term> and uses it to bootstrap an <term> | unsupervised algorithm | </term> to build the <term> Arabic word segmenter | #4657 Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap anunsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus. | |
| tech,22-2-P03-1051,ak | unsupervised algorithm </term> to build the <term> | Arabic word segmenter | </term> from a <term> large unsegmented Arabic | #4662 Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build theArabic word segmenter from a large unsegmented Arabic corpus. | |
| lr,27-2-P03-1051,ak | Arabic word segmenter </term> from a <term> | large unsegmented Arabic corpus | </term> . The <term> algorithm </term> uses a | #4667 Our method is seeded by a small manually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from alarge unsegmented Arabic corpus. | |
| tech,1-3-P03-1051,ak | unsegmented Arabic corpus </term> . The <term> | algorithm | </term> uses a <term> trigram language model | #4673 Thealgorithm uses a trigram language model to determine the most probable morpheme sequence for a given input. | |
| model,4-3-P03-1051,ak | </term> . The <term> algorithm </term> uses a <term> | trigram language model | </term> to determine the most probable <term> | #4676 The algorithm uses atrigram language model to determine the most probable morpheme sequence for a given input. | |
| other,12-3-P03-1051,ak | </term> to determine the most probable <term> | morpheme sequence | </term> for a given <term> input </term> . The | #4684 The algorithm uses a trigram language model to determine the most probablemorpheme sequence for a given input. | |
| other,17-3-P03-1051,ak | morpheme sequence </term> for a given <term> | input | </term> . The <term> language model </term> is | #4689 The algorithm uses a trigram language model to determine the most probable morpheme sequence for a giveninput. | |
| model,1-4-P03-1051,ak | for a given <term> input </term> . The <term> | language model | </term> is initially estimated from a <term> | #4692 Thelanguage model is initially estimated from a small manually segmented corpus of about 110,000 words. | |
| lr,8-4-P03-1051,ak | </term> is initially estimated from a <term> | small manually segmented corpus | </term> of about 110,000 <term> words </term> | #4699 The language model is initially estimated from asmall manually segmented corpus of about 110,000 words. | |
| other,15-4-P03-1051,ak | segmented corpus </term> of about 110,000 <term> | words | </term> . To improve the <term> segmentation | #4706 The language model is initially estimated from a small manually segmented corpus of about 110,000words. | |
| measure(ment),3-5-P03-1051,ak | <term> words </term> . To improve the <term> | segmentation accuracy | </term> , we use an <term> unsupervised algorithm | #4711 To improve thesegmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. | |
| tech,9-5-P03-1051,ak | segmentation accuracy </term> , we use an <term> | unsupervised algorithm | </term> for automatically acquiring new <term> | #4717 To improve the segmentation accuracy, we use anunsupervised algorithm for automatically acquiring new stems from a 155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. | |
| other,15-5-P03-1051,ak | </term> for automatically acquiring new <term> | stems | </term> from a <term> 155 million word unsegmented | #4723 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring newstems from a 155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. | |
| lr,18-5-P03-1051,ak | acquiring new <term> stems </term> from a <term> | 155 million word unsegmented corpus | </term> , and re-estimate the <term> model | #4726 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. |
