</term> if one or both of its neighbors is not a member of the <term> semantic set </term>
<term> speech understanding </term> , it is not appropriate to decide on a single <term>
on the <term> translation relation </term> , not as levels of <term> textual representation
speaker </term> and <term> listener </term> can not be assured to have the same <term> beliefs
statement of generalizations </term> which can not be captured in other current <term> syntax
analogies between sentences </term> : they would not be enough numerous to be of any use . We
Translation ( SMT ) </term> but which have not been addressed satisfactorily by the <term>
noting that published results to date have not been comparable across <term> corpora </term>
previously , <term> sentence extraction </term> may not capture the necessary <term> segments </term>
and conversational features </term> , but do not change the general preference of approach
</term> when their <term> meaning </term> is still not clear . This paper describes a system (
a third of the <term> sentences </term> were not covered by the <term> grammar </term> . We
shows the current <term> sentence </term> is not expected . A <term> dialogue acquisition
</term> accessible to researchers who are not experts in <term> text mining </term> . As
presented . Computer programs so far have not fared well in <term> modeling language acquisition
<term> language processing systems </term> is not geared to <term> learning </term> . We introduced
high-accuracy word-level alignment models </term> does not have a strong impact on performance . Learning
utterance </term> . The <term> user </term> does not have to speak the whole <term> sentence </term>
</term> of a <term> sentence </term> , even if not in a precise way . Another problem with
</term> over <term> parse trees </term> that were not included in the original <term> model </term>
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