| tech,4-1-H05-1012,bq | This paper presents a <term> maximum entropy word alignment algorithm </term> for <term> Arabic-English </term> based on <term> supervised training data </term> . | #7253 This paper presents amaximum entropy word alignment algorithm for Arabic-English based on supervised training data. |
| other,10-1-H05-1012,bq | This paper presents a <term> maximum entropy word alignment algorithm </term> for <term> Arabic-English </term> based on <term> supervised training data </term> . | #7259 This paper presents a maximum entropy word alignment algorithm forArabic-English based on supervised training data. |
| lr,13-1-H05-1012,bq | This paper presents a <term> maximum entropy word alignment algorithm </term> for <term> Arabic-English </term> based on <term> supervised training data </term> . | #7262 This paper presents a maximum entropy word alignment algorithm for Arabic-English based onsupervised training data. |
| lr,8-2-H05-1012,bq | 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 <term> performance </term> . | #7274 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,bq | 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 <term> performance </term> . | #7279 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,bq | 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 <term> performance </term> . | #7286 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. |
| measure(ment),26-2-H05-1012,bq | 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 <term> performance </term> . | #7292 We demonstrate that it is feasible to create training material for problems in machine translation and that a mixture of supervised and unsupervised methods yields superiorperformance. |
| model,1-3-H05-1012,bq | The <term> probabilistic model </term> used in the <term> alignment </term> directly models the <term> link decisions </term> . | #7295 Theprobabilistic model used in the alignment directly models the link decisions. |
| tech,6-3-H05-1012,bq | The <term> probabilistic model </term> used in the <term> alignment </term> directly models the <term> link decisions </term> . | #7300 The probabilistic model used in thealignment directly models the link decisions. |
| other,10-3-H05-1012,bq | The <term> probabilistic model </term> used in the <term> alignment </term> directly models the <term> link decisions </term> . | #7304 The probabilistic model used in the alignment directly models thelink decisions. |
| tech,4-4-H05-1012,bq | Significant improvement over traditional <term> word alignment techniques </term> is shown as well as improvement on several <term> machine translation tests </term> . | #7311 Significant improvement over traditionalword alignment techniques is shown as well as improvement on several machine translation tests. |
| measure(ment),15-4-H05-1012,bq | Significant improvement over traditional <term> word alignment techniques </term> is shown as well as improvement on several <term> machine translation tests </term> . | #7322 Significant improvement over traditional word alignment techniques is shown as well as improvement on severalmachine translation tests. |
| measure(ment),0-5-H05-1012,bq | Significant improvement over traditional <term> word alignment techniques </term> is shown as well as improvement on several <term> machine translation tests </term> . <term> Performance </term> of the <term> algorithm </term> is contrasted with <term> human annotation performance </term> . | #7326 Significant improvement over traditional word alignment techniques is shown as well as improvement on several machine translation tests.Performance of the algorithm is contrasted with human annotation performance. |
| tech,3-5-H05-1012,bq | <term> Performance </term> of the <term> algorithm </term> is contrasted with <term> human annotation performance </term> . | #7329 Performance of thealgorithm is contrasted with human annotation performance. |
| measure(ment),7-5-H05-1012,bq | <term> Performance </term> of the <term> algorithm </term> is contrasted with <term> human annotation performance </term> . | #7333 Performance of the algorithm is contrasted withhuman annotation performance. |
