natural language generator </term> have recently been proposed , but a fundamental concern is
</term> ) . Further , a special method has been developed for easy <term> word classification
</term> of <term> SMT models </term> has never been evaluated and compared with that of the
Translation ( SMT ) </term> but which have not been addressed satisfactorily by the <term> SMT
<term> text-understanding systems </term> have been designed under the assumption that the
<term> closed semantic domains </term> , have been developed in order to generate <term> lexical
</term> with <term> human judgment </term> has been investigated systematically on two different
English </term> . The <term> parser </term> has been implemented in <term> C++ </term> and runs
<term> utterance </term> , but they have often been disregarded , perhaps because it seemed
machine translation ( MT ) systems </term> has been considered to be more complicated than <term>
</term> . This data collection effort has been co-ordinated by <term> MADCOW ( Multi-site
be successfully obtained . An attempt has been made to use an <term> Augmented Transition
of its usefulness and usability , it has been used successfully in a research context
system response </term> to <term> users </term> has been extensively studied by the <term> natural
</term> . At MIT Lincoln Laboratory , we have been developing a <term> Korean-to-English machine
<term> Chat-80 </term> . <term> Chat-80 </term> has been designed to be both efficient and easily
understanding of <term> scruffy texts </term> has been incorporated into a working <term> computer
ways . We first apply approaches that have been proposed for <term> predicting top-level
approach . <term> Word Identification </term> has been an important and active issue in <term> Chinese
approach to <term> summarization </term> has been shown to work in <term> documents </term> of
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