| 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,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,30-7-P03-1051,ak | manually segmented corpus </term> of the <term> | language | </term> of interest . A central problem of | #4797 We believe this is a state-of-the-art performance and the algorithm can be used for many highly inflected languages provided that one can create a small manually segmented corpus of thelanguage of interest. | |
| other,32-5-P03-1051,ak | parameters </term> with the expanded <term> | vocabulary | </term> and <term> training corpus </term> . | #4740 To improve the segmentation 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 expandedvocabulary and training corpus. | |
| 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). | |
| 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. | |
| 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,2-6-P03-1051,ak | training corpus </term> . The resulting <term> | Arabic word segmentation system | </term> achieves around 97 % <term> exact match | #4747 The resultingArabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens. | |
| 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. | |
| 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. | |
| tech,9-7-P03-1051,ak | state-of-the-art performance and the <term> | algorithm | </term> can be used for many <term> highly | #4776 We believe this is a state-of-the-art performance and thealgorithm can be used for many highly inflected languages provided that one can create a small manually segmented corpus of the language of interest. |
