| 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 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. | |
| 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 and training corpus . | |
| 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 a test corpus containing 28,449 word tokens. | |
| 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 probable morpheme sequence for a given input. | |
| model,1-4-P03-1051,ak | for a given <term> input </term> . The <term> | language model | </term> is initially estimated from a <term> | #4692 The language model is initially estimated from a small manually segmented corpus of about 110,000 words. |
