| 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. | |
| 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. | |
| lr,34-5-P03-1051,ak | expanded <term> vocabulary </term> and <term> | training corpus | </term> . The resulting <term> Arabic word | #4742 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 expanded vocabulary andtraining 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. | |
| 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. | |
| lr,15-6-P03-1051,ak | <term> exact match accuracy </term> on a <term> | test corpus | </term> containing 28,449 <term> word tokens | #4760 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on atest corpus containing 28,449 word tokens. | |
| other,19-6-P03-1051,ak | test corpus </term> containing 28,449 <term> | word tokens | </term> . We believe this is a state-of-the-art | #4764 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449word tokens. | |
| 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. | |
| 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. | |
| 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. | |
| 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. |
