D14-1113 |
word sense discrimination and
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embedding
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learning , by non-parametrically
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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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D14-1167 |
propose a novel method of jointly
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embedding
/embed/VBG
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entities and words into the same
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D14-1167 |
continuous vector space . The
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embedding
/embedding/NN
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process attempts to preserve
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D14-1167 |
Times corpus show that jointly
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embedding
/embed/VBG
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brings promising improvement
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D14-1167 |
facts , compared to separately
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embedding
/embed/VBG
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knowledge graphs and text . Particularly
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D14-1167 |
text . Particularly , jointly
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embedding
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enables the prediction of facts
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D14-1167 |
can not be handled by previous
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embedding
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methods . At the same time ,
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D14-1167 |
reasoning task show that jointly
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embedding
/embed/VBG
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is comparable to or slightly
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D15-1029 |
dot product between each word
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embedding
/embedding/NN
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and part of the first hidden
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D15-1029 |
network architecture where the
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embedding
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layer feeds into multiple hidden
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D15-1031 |
We study the problem of jointly
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embedding
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a knowledge base and a text corpus
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D15-1031 |
dependency on anchors . We require the
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embedding
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vector of an entity not only
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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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D15-1034 |
relationships as translations in the
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embedding
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space , have shown promising
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D15-1036 |
evaluation methods for unsupervised
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embedding
/embedding/NN
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techniques that obtain meaningful
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D15-1036 |
result in different orderings of
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embedding
/embedding/NN
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methods , calling into question
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D15-1038 |
completion impute missing facts by
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embedding
/embed/VBG
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knowledge graphs in vector spaces
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D15-1054 |
is to explore the use of word
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embedding
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techniques to generate effective
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D15-1054 |
application of conventional word
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embedding
/embedding/NN
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methodologies for ad click prediction
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