| 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. | |
| 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. | |
| 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. | |
| 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. | |
| 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. | |
| model,27-5-P03-1051,ak | corpus </term> , and re-estimate the <term> | model parameters | </term> with the expanded <term> vocabulary | #4735 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 themodel 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. | |
| 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. | |
| 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,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,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. |
