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some learned representation (
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) , and ( 3 ) compute output
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D14-1062 |
relevance for the in-domain task . By
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our latent domain phrase model
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D14-1113 |
word sense discrimination and
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learning , by non-parametrically
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D14-1167 |
Zheng Abstract We examine the
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approach to reason new relational
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D15-1029 |
dot product between each word
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and part of the first hidden
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D15-1031 |
We study the problem of jointly
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a knowledge base and a text corpus
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D15-1034 |
relationships as translations in the
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space , have shown promising
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D15-1036 |
evaluation methods for unsupervised
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techniques that obtain meaningful
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D15-1038 |
completion impute missing facts by
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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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techniques to generate effective
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D15-1098 |
component-enhanced Chinese character
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models and their bigram extensions
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D15-1153 |
previous work on integrating word
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features into a discrete linear
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D15-1183 |
Chunyan Abstract Most existing word
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methods can be categorized into
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Abstract We consider the problem of
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knowledge graphs ( KGs ) into
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outperforms the state-of-the-art word
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methods in both representation
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D15-1200 |
paper we introduce a multisense
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model based on Chinese Restaurant
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</title> R Abstract Compositional
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models build a representation
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D15-1232 |
words , where we assume that an
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of each word can represent its
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D15-1246 |
there has been a surge of word
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algorithms and research on them
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, penalizing embeddings , re -
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words , and dropout . We also
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