| 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,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. | |
| lr,24-7-P03-1051,ak | </term> provided that one can create a <term> | small manually segmented corpus | </term> of the <term> language </term> of interest | #4791 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 asmall manually segmented corpus of the language of interest. | |
| measure(ment),10-6-P03-1051,ak | system </term> achieves around 97 % <term> | exact match accuracy | </term> on a <term> test corpus </term> containing | #4755 The resulting Arabic word segmentation system achieves around 97%exact match accuracy on a test corpus containing 28,449 word tokens. | |
| 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,15-7-P03-1051,ak | algorithm </term> can be used for many <term> | highly inflected languages | </term> provided that one can create a <term> | #4782 We believe this is a state-of-the-art performance and the algorithm can be used for manyhighly 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. | |
| 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,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. |
