utterance classification </term> that does not require <term> manual transcription </term>
high-accuracy word-level alignment models </term> does not have a strong impact on performance . Learning
<term> speech understanding </term> , it is not appropriate to decide on a single <term>
noting that published results to date have not been comparable across <term> corpora </term>
</term> . However , such an approach does not work well when there is no distinctive <term>
Our study reveals that the proposed method not only reduces an extensive system development
analogies between sentences </term> : they would not be enough numerous to be of any use . We
in each author 's <term> corpus </term> tend not to be <term> synonymous expressions </term>
previously , <term> sentence extraction </term> may not capture the necessary <term> segments </term>
</term> over <term> parse trees </term> that were not included in the original <term> model </term>
</term> , can reliably determine whether or not they are <term> translations </term> of each
<term> word sense disambiguation </term> does not yield significantly better <term> translation
Translation ( SMT ) </term> but which have not been addressed satisfactorily by the <term>
and conversational features </term> , but do not change the general preference of approach
</term> accessible to researchers who are not experts in <term> text mining </term> . As
words </term> , the system guesses correctly not placing <term> commas </term> with a <term> precision
FROFF </term> which can make a fair copy of not only texts but also graphs and tables indispensable
direct imitation of human performance is not the best way to implement many of these
language learning </term> . However , this is not the only area in which the principles of
</term> of a <term> sentence </term> , even if not in a precise way . Another problem with
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