</term> . However , such an approach does not work well when there is no distinctive <term>
applicable in <term> general domains </term> does not readily lend itself in the <term> linguistic
<term> word sense disambiguation </term> does not yield significantly better <term> translation
words </term> , the system guesses correctly not placing <term> commas </term> with a <term> precision
on the <term> translation relation </term> , not as levels of <term> textual representation
for <term> document classification </term> has not produced significant improvements in performance
direct imitation of human performance is not the best way to implement many of these
sophisticated <term> annotation </term> , and does not require a <term> pre-tagged corpus </term>
error for the purposes of correction does not use any concepts of the underlying <term>
utterance </term> . The <term> user </term> does not have to speak the whole <term> sentence </term>
a third of the <term> sentences </term> were not covered by the <term> grammar </term> . We
</term> if one or both of its neighbors is not a member of the <term> semantic set </term>
</term> of a <term> sentence </term> , even if not in a precise way . Another problem with
<term> speech understanding </term> , it is not appropriate to decide on a single <term>
utterance classification </term> that does not require <term> manual transcription </term>
<term> semantic questions </term> that we do not yet have . <term> Semantic </term> and other
statement of generalizations </term> which can not be captured in other current <term> syntax
language learning </term> . However , this is not the only area in which the principles of
disambiguation algorithms </term> that did not make use of the <term> discourse constraint
previously , <term> sentence extraction </term> may not capture the necessary <term> segments </term>
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