tech,4-1-H05-1012,bq phrases </term> . This paper presents a <term> maximum entropy word alignment algorithm </term> for <term> Arabic-English </term> based
other,10-1-H05-1012,bq word alignment algorithm </term> for <term> Arabic-English </term> based on <term> supervised training
lr,13-1-H05-1012,bq <term> Arabic-English </term> based on <term> supervised training data </term> . We demonstrate that it is feasible
lr,8-2-H05-1012,bq demonstrate that it is feasible to create <term> training material </term> for problems in <term> machine translation
tech,13-2-H05-1012,bq training material </term> for problems in <term> machine translation </term> and that a mixture of <term> supervised
tech,20-2-H05-1012,bq translation </term> and that a mixture of <term> supervised and unsupervised methods </term> yields superior <term> performance </term>
measure(ment),26-2-H05-1012,bq unsupervised methods </term> yields superior <term> performance </term> . The <term> probabilistic model </term>
model,1-3-H05-1012,bq superior <term> performance </term> . The <term> probabilistic model </term> used in the <term> alignment </term>
tech,6-3-H05-1012,bq probabilistic model </term> used in the <term> alignment </term> directly models the <term> link decisions
other,10-3-H05-1012,bq alignment </term> directly models the <term> link decisions </term> . Significant improvement over traditional
tech,4-4-H05-1012,bq Significant improvement over traditional <term> word alignment techniques </term> is shown as well as improvement on
measure(ment),15-4-H05-1012,bq as well as improvement on several <term> machine translation tests </term> . <term> Performance </term> of the <term>
measure(ment),0-5-H05-1012,bq <term> machine translation tests </term> . <term> Performance </term> of the <term> algorithm </term> is contrasted
tech,3-5-H05-1012,bq </term> . <term> Performance </term> of the <term> algorithm </term> is contrasted with <term> human annotation
measure(ment),7-5-H05-1012,bq algorithm </term> is contrasted with <term> human annotation performance </term> . This paper presents a <term> phrase-based
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