N07-1067 |
of documents is received , the
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answer generator
|
module selects candidate pas
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W06-1905 |
Answer Generator The task of the
|
Answer Generator
|
( AG ) module is to produce a
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N07-1067 |
average ( Table 1 ) . Moreover , our
|
answer generator
|
seemed to adapt well to information
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C88-2119 |
generator . Construction of this
|
answer generator
|
is a future project . The generator
|
W06-1908 |
query frame has its corresponding
|
Answer generator
|
. We use template based answer
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W02-0507 |
closely matches the query . The
|
Answer Generator
|
looks for keywords that might
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C88-2119 |
significance for propositions . An
|
answer generator
|
that combines propositions ,
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W03-2309 |
to the query . The intensional
|
answer generator
|
either decides that no suitable
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W06-1905 |
( Nyberg , et al. 2005 ) . 3.5
|
Answer Generator
|
The task of the Answer Generator
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W01-1620 |
representation , is sent to the
|
answer generator
|
and then to the synthesizer .
|
C88-2119 |
propositions axe passed to an
|
answer generator
|
. Construction of this answer
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W02-0507 |
in QARAB The input to the QARAB
|
Answer Generator
|
module is a natural language
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J07-1005 |
) . Furthermore , the current
|
answer generator
|
does not handle complex issues
|
W09-2703 |
two modules : Answer Ranker and
|
Answer Generator
|
. Answer Ranker concerns with
|
W02-0507 |
names keywords . The input to the
|
Answer Generator
|
is the " bag of words " and the
|
J07-1005 |
see Section 6 ) . Finally , the
|
answer generator
|
takes these citations and extracts
|
W06-1905 |
language-independent extraction algorithms . The
|
Answer Generator
|
uses language-specific sub-modules
|
W09-3948 |
carry out the query . • The
|
answer generator
|
provides a error warning when
|
W09-2703 |
Dr. As for the final step , the
|
Answer Generator
|
module formats the top five candidate
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E87-1033 |
robusteness component , and the NL
|
answer generator
|
, are still being developed .
|