J10-3010 |
extrinsic evaluation is done by
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embedding
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the expansion systems into a
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D15-1054 |
successful application of word
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embedding
|
techniques for the task of click
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H90-1008 |
rule causes retraversal of the
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embedding
|
link , and the Past-rule then
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D15-1200 |
relat - edness , controlling for
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embedding
|
dimen - sionality . We find that
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P15-1107 |
that hierarchically builds an
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embedding
|
for a paragraph from embeddings
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D15-1205 |
models build a representation ( or
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embedding
|
) for a linguistic structure
|
D14-1062 |
relevance for the in-domain task . By
|
embedding
|
our latent domain phrase model
|
J98-4006 |
especially those involving central
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embedding
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( recursion ) , was one of the
|
E89-1033 |
has been implemented , and an
|
embedding
|
of this in an interactive parsing
|
D15-1054 |
propose a set of novel joint word
|
embedding
|
methods by leveraging implicit
|
P15-2108 |
closest to it in a particular
|
embedding
|
provides a characterization for
|
H90-1008 |
predicates to be at about the time of
|
embedding
|
event ( e.g. , the assertion
|
W15-3822 |
: Left-Right surrounding based
|
embedding
|
feature ( LR_SBE ) and MAX surrounding
|
S14-2011 |
provides dense , low-dimensional
|
embedding
|
for each fragment which allows
|
D14-1012 |
effectively incorporating the word
|
embedding
|
features within the framework
|
W15-1501 |
on the popular skip-gram word
|
embedding
|
model . The novelty of our approach
|
D15-1200 |
highlight the importance of testing
|
embedding
|
models in real applications .
|
W15-1504 |
The method , Instance-context
|
embedding
|
( ICE ) , leverages neural word
|
D14-1167 |
continuous vector space . The
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embedding
|
process attempts to preserve
|
N06-4008 |
makes mistakes ) . Ndaona includes
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embedding
|
and graphics parameter estimation
|