| model,8-1-P03-1051,bq | Arabic 's rich morphology </term> by a <term> | model | </term> that a <term> word </term> consists | #4608 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 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 a word consists of a sequence of morphemes in the pattern prefix*-stem-suffix* (* denotes zero or more occurrences of a morpheme). |
| other,20-1-P03-1051,bq | sequence of <term> morphemes </term> in the <term> | pattern | </term> <term> prefix * - stem-suffix * </term> | #4620 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 a morpheme). |
| other,35-1-P03-1051,bq | denotes zero or more occurrences of a <term> | morpheme | </term> ) . Our method is seeded by a small | #4635 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 a morpheme ). |
| tech,1-3-P03-1051,bq | unsegmented Arabic corpus </term> . The <term> | algorithm | </term> uses a <term> trigram language model | #4671 The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input. |
| other,17-3-P03-1051,bq | morpheme sequence </term> for a given <term> | input | </term> . The <term> language model </term> | #4687 The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input . |
| tech,3-5-P03-1051,bq | <term> words </term> . To improve the <term> | segmentation | </term> <term> accuracy </term> , we use an | #4709 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 and training corpus. |
| 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 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 and training corpus. |
| other,20-5-P03-1051,bq | <term> stems </term> from a 155 million <term> | word | </term> <term> unsegmented corpus </term> , | #4726 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 and training corpus. |
| other,32-5-P03-1051,bq | parameters </term> with the expanded <term> | vocabulary | </term> and <term> training corpus </term> . | #4738 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 and training corpus. |
| tech,9-7-P03-1051,bq | state-of-the-art performance and the <term> | algorithm | </term> can be used for many <term> highly | #4774 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 the language of interest. |
| other,30-7-P03-1051,bq | manually segmented corpus </term> of the <term> | language | </term> of interest . A central problem | #4795 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 the language of interest. |
