J13-1008 |
complex syntactic patterns , and
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embedding
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useful morphological features
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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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C82-2022 |
of PP 's , for which both the "
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embedding
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" and the " same-level " hypotheses
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D15-1191 |
Abstract We consider the problem of
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embedding
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knowledge graphs ( KGs ) into
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W05-0627 |
the largest probability among
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embedding
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ones are kept . After predicting
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P14-1011 |
learns how to transform semantic
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embedding
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space in one language to the
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C86-1088 |
who owns a book reads it . The
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embedding
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rule for = > - conditions
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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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P15-1107 |
and words , then decodes this
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embedding
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to reconstruct the original paragraph
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H91-1024 |
there is , for example , much more
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embedding
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of requests in hypotheticals
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J80-1001 |
grammars have a straightforward
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embedding
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, but which permit various transformations
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D14-1062 |
relevance for the in-domain task . By
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embedding
|
our latent domain phrase model
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J09-1002 |
TransType ideas , the innovative
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embedding
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proposed here consists in using
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C80-1009 |
construction capable of unlimited
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embedding
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. The results of this treatment
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D15-1205 |
</title> R Abstract Compositional
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embedding
|
models build a representation
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D15-1036 |
result in different orderings of
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embedding
|
methods , calling into question
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D15-1054 |
application of conventional word
|
embedding
|
methodologies for ad click prediction
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P15-1125 |
large-scale knowledge bases . The novel
|
embedding
|
model associates each category
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P15-1009 |
lie close to each other in the
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embedding
|
space . Two manifold learning
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J82-3001 |
different . The intuitive notion of "
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embedding
|
a linguistic theory into a model
|