| tech,4-1-H05-1012,ak | This paper presents a <term> maximum entropy word alignment algorithm </term> for <term> Arabic-English </term> based on <term> supervised training data </term> . | #5279 This paper presents amaximum entropy word alignment algorithm for Arabic-English based on supervised training data. | |
| other,10-1-H05-1012,ak | This paper presents a <term> maximum entropy word alignment algorithm </term> for <term> Arabic-English </term> based on <term> supervised training data </term> . | #5285 This paper presents a maximum entropy word alignment algorithm forArabic-English based on supervised training data. | |
| lr,13-1-H05-1012,ak | This paper presents a <term> maximum entropy word alignment algorithm </term> for <term> Arabic-English </term> based on <term> supervised training data </term> . | #5288 This paper presents a maximum entropy word alignment algorithm for Arabic-English based onsupervised training data. | |
| lr,8-2-H05-1012,ak | We demonstrate that it is feasible to create <term> training material </term> for problems in <term> machine translation </term> and that a mixture of <term> supervised and unsupervised methods </term> yields superior performance . | #5300 We demonstrate that it is feasible to createtraining material for problems in machine translation and that a mixture of supervised and unsupervised methods yields superior performance. | |
| tech,13-2-H05-1012,ak | We demonstrate that it is feasible to create <term> training material </term> for problems in <term> machine translation </term> and that a mixture of <term> supervised and unsupervised methods </term> yields superior performance . | #5305 We demonstrate that it is feasible to create training material for problems inmachine translation and that a mixture of supervised and unsupervised methods yields superior performance. | |
| tech,20-2-H05-1012,ak | We demonstrate that it is feasible to create <term> training material </term> for problems in <term> machine translation </term> and that a mixture of <term> supervised and unsupervised methods </term> yields superior performance . | #5312 We demonstrate that it is feasible to create training material for problems in machine translation and that a mixture ofsupervised and unsupervised methods yields superior performance. | |
| model,1-3-H05-1012,ak | The <term> probabilistic model </term> used in the <term> alignment </term> directly models the link decisions . | #5321 Theprobabilistic model used in the alignment directly models the link decisions. | |
| tech,6-3-H05-1012,ak | The <term> probabilistic model </term> used in the <term> alignment </term> directly models the link decisions . | #5326 The probabilistic model used in thealignment directly models the link decisions. | |
| tech,3-4-H05-1012,ak | Significant improvement over <term> traditional word alignment techniques </term> is shown as well as improvement on several <term> machine translation tests </term> . | #5336 Significant improvement overtraditional word alignment techniques is shown as well as improvement on several machine translation tests. | |
| measure(ment),15-4-H05-1012,ak | Significant improvement over <term> traditional word alignment techniques </term> is shown as well as improvement on several <term> machine translation tests </term> . | #5348 Significant improvement over traditional word alignment techniques is shown as well as improvement on severalmachine translation tests. | |
| tech,3-5-H05-1012,ak | Performance of the <term> algorithm </term> is contrasted with <term> human annotation performance </term> . | #5355 Performance of thealgorithm is contrasted with human annotation performance. | |
| measure(ment),7-5-H05-1012,ak | Performance of the <term> algorithm </term> is contrasted with <term> human annotation performance </term> . | #5359 Performance of the algorithm is contrasted withhuman annotation performance. |
