it is also time consuming to document . Given the development of <term> storage media and
translation output </term> . Subjects were given a set of up to six extracts of <term> translated
translation outputs </term> . The subjects were given three minutes per extract to determine
possible <term> sentence plans </term> for a given <term> text-plan input </term> . Second , the
<term> wide coverage English grammar </term> are given . While <term> paraphrasing </term> is critical
off-the-shelf <term> classifiers </term> to give <term> utterance classification performance
</term> as either coherent or incoherent ( given a <term> baseline </term> of 54.55 % ) . We
together , the resulting <term> tagger </term> gives a 97.24 % <term> accuracy </term> on the <term>
multilingual , multimedia data </term> . It gives users the ability to spend their time finding
finding more data relevant to their task , and gives them translingual reach into other <term>
likely <term> answer candidates </term> from the given <term> text corpus </term> . The operation
genre </term> . Examples and results will be given for <term> Arabic </term> , but the approach
probable <term> morpheme sequence </term> for a given <term> input </term> . The <term> language model
<term> sentences </term> that it contains . We give two estimates , a lower one and a higher
acquiring <term> English topic signatures </term> . Given a particular <term> concept </term> , or <term>
evaluations have shown , that <term> SMT </term> gives competitive results to <term> rule-based
domains </term> . This workshop is intended to give an introduction to <term> statistical machine
entailment </term> . Our <term> technique </term> gives a substantial improvement in <term> paraphrase
<term> maximum entropy classifier </term> that , given a pair of <term> sentences </term> , can reliably
like reproducability and independency of a given biased <term> gold standard </term> it also
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