| lr,15-6-P03-1051,bq | <term> exact match accuracy </term> on a <term> | test corpus | </term> containing 28,449 <term> word tokens | #4758 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on atest corpus containing 28,449 word tokens. |
| lr,21-5-P03-1051,bq | from a 155 million <term> word </term><term> | unsegmented corpus | </term> , and re-estimate the <term> model | #4727 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 million wordunsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. |
| lr,25-7-P03-1051,bq | provided that one can create a small <term> | manually segmented corpus | </term> of the <term> language </term> of interest | #4790 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 smallmanually segmented corpus of the language of interest. |
| lr,28-2-P03-1051,bq | word segmenter </term> from a large <term> | unsegmented Arabic corpus | </term> . The <term> algorithm </term> uses a | #4666 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 a largeunsegmented Arabic corpus. |
| lr,34-5-P03-1051,bq | expanded <term> vocabulary </term> and <term> | training corpus | </term> . The resulting <term> Arabic word | #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 expanded vocabulary andtraining corpus. |
| lr,7-2-P03-1051,bq | . Our method is seeded by a small <term> | manually segmented Arabic corpus | </term> and uses it to bootstrap an <term> | #4645 Our method is seeded by a smallmanually segmented Arabic corpus and uses it to bootstrap an unsupervised algorithm to build the Arabic word segmenter from a large unsegmented Arabic corpus. |
| lr,9-4-P03-1051,bq | is initially estimated from a small <term> | manually segmented corpus | </term> of about 110,000 <term> words </term> | #4698 The language model is initially estimated from a smallmanually segmented corpus of about 110,000 words. |
| measure(ment),10-6-P03-1051,bq | system </term> achieves around 97 % <term> | exact match accuracy | </term> on a <term> test corpus </term> containing | #4753 The resulting Arabic word segmentation system achieves around 97%exact match accuracy on a test corpus containing 28,449 word tokens. |
| measure(ment),4-5-P03-1051,bq | improve the <term> segmentation </term><term> | accuracy | </term> , we use an <term> unsupervised algorithm | #4710 To improve the segmentationaccuracy, 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. |
| model,1-4-P03-1051,bq | for a given <term> input </term> . The <term> | language model | </term> is initially estimated from a small | #4690 Thelanguage model is initially estimated from a small manually segmented corpus of about 110,000 words. |
| model,4-3-P03-1051,bq | </term> . The <term> algorithm </term> uses a <term> | trigram language model | </term> to determine the most probable <term> | #4674 The algorithm uses atrigram language model to determine the most probable morpheme sequence for a given input. |
| model,8-1-P03-1051,bq | Arabic 's rich morphology </term> by a <term> | model | </term> that a <term> word </term> consists of | #4608 We approximate Arabic's rich morphology by amodel 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,bq | </term> by a <term> model </term> that a <term> | word | </term> consists of a sequence of <term> morphemes | #4611 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,12-3-P03-1051,bq | </term> to determine the most probable <term> | morpheme sequence | </term> for a given <term> input </term> . The | #4682 The algorithm uses a trigram language model to determine the most probablemorpheme sequence for a given input. |
| other,15-4-P03-1051,bq | segmented corpus </term> of about 110,000 <term> | words | </term> . To improve the <term> segmentation | #4704 The language model is initially estimated from a small manually segmented corpus of about 110,000words. |
| other,15-5-P03-1051,bq | </term> for automatically acquiring new <term> | stems | </term> from a 155 million <term> word </term> | #4721 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. |
| other,15-7-P03-1051,bq | algorithm </term> can be used for many <term> | highly inflected languages | </term> provided that one can create a small | #4780 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. |
| other,17-1-P03-1051,bq | word </term> consists of a sequence of <term> | morphemes | </term> in the <term> pattern </term><term> prefix | #4617 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,17-3-P03-1051,bq | morpheme sequence </term> for a given <term> | input | </term> . The <term> language model </term> is | #4687 The algorithm uses a trigram language model to determine the most probable morpheme sequence for a giveninput. |
| other,19-6-P03-1051,bq | test corpus </term> containing 28,449 <term> | word tokens | </term> . We believe this is a state-of-the-art | #4762 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449word tokens. |
