| other,12-3-P03-1051,bq | The <term> algorithm </term> uses a <term> trigram language model </term> to determine the most probable <term> morpheme sequence </term> for a given <term> input </term> . | #4682 The algorithm uses a trigram language model to determine the most probable morpheme sequence for a given input. |
| model,1-4-P03-1051,bq | The <term> language model </term> is initially estimated from a small <term> manually segmented corpus </term> of about 110,000 <term> words </term> . | #4690 The language model is initially estimated from a small manually segmented corpus of about 110,000 words. |
| lr,34-5-P03-1051,bq | To improve the <term> segmentation </term><term> accuracy </term> , we use an <term> unsupervised algorithm </term> for automatically acquiring new <term> stems </term> from a 155 million <term> word </term><term> unsegmented corpus </term> , and re-estimate the <term> model 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 expanded vocabulary and training corpus . |
| lr,15-6-P03-1051,bq | The resulting <term> Arabic word segmentation system </term> achieves around 97 % <term> exact match accuracy </term> on a <term> test corpus </term> containing 28,449 <term> word tokens </term> . | #4758 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens. |
