C80-1074 |
be the nominalization and the
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embedding
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operator respectively . An arbitrary
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A00-2015 |
subordinate clauses , in which scope
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embedding
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preference of subordinate clauses
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D12-1086 |
based on Euclidean co-occurrence
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embedding
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combines the paradigmatic context
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C90-2071 |
considerations arising from the
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embedding
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phrase and context . Figure 1
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C80-1074 |
operator ( Fill , FII 2 and FI21 ) ,
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embedding
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operator fill and connecting
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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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C92-2095 |
eliminate unnecessary center -
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embedding
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; and ( 3 ) eliminating of scrambling
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C80-1074 |
Fl ) , nominalization ( FII ) ,
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embedding
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( fill ) , connecting ( FIV )
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C92-2072 |
thus , we would include multiple
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embedding
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constructions , poten - ACT ,
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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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D14-1012 |
prototype approach , for utilizing the
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embedding
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features . The presented approaches
|
D14-1012 |
approaches can better utilize the word
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embedding
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features , among which the distributional
|
D14-1012 |
outperforming the dense and continuous
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embedding
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features by nearly 2 points of
|
D14-1015 |
investigate how to improve bilingual
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embedding
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which has been successfully used
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D14-1015 |
translation ( SMT ) . Despite bilingual
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embedding
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's success , the contextual information
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D14-1015 |
memory-efficient model for learning bilingual
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embedding
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, taking both the source phrase
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D14-1015 |
generated from our proposed bilingual
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embedding
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model are used as features in
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D14-1030 |
some learned representation (
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embedding
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) , and ( 3 ) compute output
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D14-1030 |
Sec - ondly , the neural network
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embedding
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of word i can predict the MEG
|
D14-1062 |
relevance for the in-domain task . By
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embedding
|
our latent domain phrase model
|