D15-1038 |
completion impute missing facts by
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embedding
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knowledge graphs in vector spaces
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H90-1008 |
the second only does so if no
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embedding
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link exists at the current focus
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J14-2006 |
hidden in a cover text using the
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embedding
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algorithm , resulting in the
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W06-0603 |
marker presence and syntactic
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embedding
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structure to be strongly associated
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C92-3137 |
re-Evaluation of the attitude in the
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embedding
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attitude contexts . Thus , ill
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D14-1167 |
Zheng Abstract We examine the
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embedding
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approach to reason new relational
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D15-1034 |
relationships as translations in the
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embedding
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space , have shown promising
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P14-1011 |
learns how to transform semantic
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embedding
|
space in one language to the
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P15-1077 |
Gaussian distributions on the
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embedding
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space . This encourages the model
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S15-2085 |
, word prior polarities , and
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embedding
|
clusters . Using weighted Support
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D15-1031 |
KBs but also to be equal to the
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embedding
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vector computed from the text
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D14-1012 |
prototype approach , for utilizing the
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embedding
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features . The presented approaches
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S14-2011 |
provides dense , low-dimensional
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embedding
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for each fragment which allows
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C80-1074 |
Extract the variables J Is the
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embedding
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operator applied te the predicate
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P13-1017 |
model , in which bilingual word
|
embedding
|
is discriminatively learnt to
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W03-2200 |
directions for improving MT by
|
embedding
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it in an environment of other
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C92-2072 |
thus , we would include multiple
|
embedding
|
constructions , poten - ACT ,
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D14-1062 |
relevance for the in-domain task . By
|
embedding
|
our latent domain phrase model
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P15-1009 |
regularization terms to constrain the
|
embedding
|
task . We empirically evaluate
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W15-2608 |
based approach that uses word
|
embedding
|
features to recognize drug names
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