. In this paper we show how two standard outputs from <term> information extraction ( IE )
other,18-6-H01-1041,bq system </term> produces the <term> translation output </term> sufficient for content understanding
other,28-1-H01-1042,bq human language learners </term> , to the <term> output </term> of <term> machine translation ( MT
sentence-plan-ranker ( SPR ) </term> ranks the list of output <term> sentence plans </term> , and then selects
the <term> shared derivation forest </term> output by a prior <term> RCL parser </term> for a
other,21-3-N03-1001,bq particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this
other,38-1-N03-1018,bq through its transformation into the <term> noisy output </term> of an <term> OCR system </term> . The
tech,26-2-N03-1026,bq maximum-entropy model </term> for <term> stochastic output selection </term> . Furthermore , we propose
</term> are in <term> Arabic </term> , and the output <term> summary </term> is in <term> English </term>
measure(ment),6-3-H05-1117,bq <term> methods </term> for <term> scoring system output </term> is an impediment to progress in the
other,30-2-H05-2007,bq patterns </term> in <term> machine translation output </term> . Automatic <term> evaluation metrics
directly compare commercial <term> systems </term> outputting <term> unsegmented texts </term> with , for
other,11-3-I05-6011,bq </term> and improving <term> machine translation outputs </term> . Annotating <term> honorifics </term>
article considers approaches which rerank the output of an existing <term> probabilistic parser
tech,3-7-P05-1067,bq <term> model </term> . We evaluate the <term> outputs </term> of our <term> MT system </term> using
other,12-4-P05-2016,bq </term> and plan to compare our <term> system 's output </term> with a <term> benchmark system </term>
other,9-4-E06-1035,bq <term> performance </term> of using <term> ASR output </term> as opposed to <term> human transcription
other,18-1-P06-4014,bq pipeline </term> that capitalizes on <term> output quality </term> . The demonstrator embodies
</term> of a <term> parser </term> 's multiple output . Some examples of <term> paraphrasing </term>
by gathering <term> statistics </term> on the output of other <term> linguistic tools </term> .
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