| 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). | |
| 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. | |
| other,12-3-P03-1051,ak | </term> to determine the most probable <term> | morpheme sequence | </term> for a given <term> input </term> . The | #4684 The algorithm uses a trigram language model to determine the most probablemorpheme sequence for a given input. | |
| other,15-4-P03-1051,ak | segmented corpus </term> of about 110,000 <term> | words | </term> . To improve the <term> segmentation | #4706 The language model is initially estimated from a small manually segmented corpus of about 110,000words. | |
| 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. | |
| measure(ment),3-5-P03-1051,ak | <term> words </term> . To improve the <term> | segmentation accuracy | </term> , we use an <term> unsupervised algorithm | #4711 To improve thesegmentation 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 and training 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. | |
| 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,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,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. | |
| 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). | |
| 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. | |
| lr,18-5-P03-1051,ak | acquiring new <term> stems </term> from a <term> | 155 million word unsegmented corpus | </term> , and re-estimate the <term> model | #4726 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. | |
| other,17-1-P03-1051,ak | word </term> consists of a sequence of <term> | morphemes | </term> in the <term> pattern </term> prefix | #4619 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,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). | |
| 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. | |
| 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,15-5-P03-1051,ak | </term> for automatically acquiring new <term> | stems | </term> from a <term> 155 million word unsegmented | #4723 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. | |
| 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,27-2-P03-1051,ak | Arabic word segmenter </term> from a <term> | large unsegmented Arabic corpus | </term> . The <term> algorithm </term> uses a | #4667 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 alarge unsegmented Arabic corpus. |
