A00-2003 |
active research topic in Natural
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Language Generation
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. Adequate referring expressions
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A00-2026 |
trainable systems for surface natural
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language generation
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( NLG ) . Surface NLG , for our
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A92-1010 |
discuss the addition of natural
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language generation
|
to the interaction tools provided
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A92-1010 |
towards the integration of natural
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language generation
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to augment the interaction functionalities
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A00-2034 |
meaning are important in natural
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language generation
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( Stede , 1998 ) . Alternations
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A00-2026 |
three systems for surface natural
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language generation
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that are trainable from annotated
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A00-1010 |
dialogue and discourse management ,
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language generation
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and language understanding components
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A00-2026 |
Trainable Methods for Surface Natural
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Language Generation
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</title> Adwait Ratnaparkhi Abstract
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A88-1029 |
posing no significant natural
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language generation
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problems . Our main concern is
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A88-1006 |
goals are often at odds . Natural
|
language generation
|
is no exception . The years of
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A88-1006 |
grammatical treatments in natural
|
language generation
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. We present a method for building
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A00-3001 |
Hua Cheng ac Abstract In natural
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language generation
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, different generation tasks
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A00-1017 |
information that components in a natural
|
language generation
|
( NLG ) system will need to communicate
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A92-1036 |
reports on the SPOKESMAN natural
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language generation
|
system , which is a domain independent
|
A92-1036 |
grammatical treatments in natural
|
language generation
|
. 1988 Architecture : Interfacing
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A00-1009 |
monolingual or multilingual natural
|
language generation
|
( NLG ) applications and machine
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A00-1010 |
appropriate to the situation . 4
|
Language Generation
|
The language generator takes
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A88-1034 |
linguistic choices and gestural ones in
|
language generation
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models . called Lucy ( Rich et
|
A00-3001 |
Introduction In automatic natural
|
language generation
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( NLG ) , various versions of
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A88-1001 |
resources to a full-scale natural
|
language generation
|
effort . In verbose mode , a
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