J98-4006 |
especially those involving central
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embedding
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( recursion ) , was one of the
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P14-1146 |
learning sentiment - specific word
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embedding
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( SSWE ) , which encodes sentiment
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S14-2033 |
concatenating the sentiment-specific word
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embedding
/embedding/NN
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( SSWE ) features with the state-of-the-art
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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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D15-1205 |
models build a representation ( or
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embedding
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) for a linguistic structure
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D15-1031 |
We study the problem of jointly
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embedding
/embed/VBG
|
a knowledge base and a text corpus
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J82-3001 |
different . The intuitive notion of "
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embedding
/embed/VBG
|
a linguistic theory into a model
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T78-1035 |
recognized the desirability of
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embedding
/embed/VBG
|
a theory of grammar within a
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P15-2098 |
ranking . We show that radical
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embedding
/embedding/NN
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achieves com - parable , and
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D15-1306 |
and incorporating frame-semantic
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embedding
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achieves the best overall performance
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E09-3009 |
Vector Space Model ( VSM ) by
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embedding
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additional types of information
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J14-2006 |
hidden in a cover text using the
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embedding
/embedding/NN
|
algorithm , resulting in the
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P98-2242 |
given , which are realised in an
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embedding
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algorithm . The significant aspect
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D15-1246 |
there has been a surge of word
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embedding
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algorithms and research on them
|
P98-1016 |
of the clustering is a large CG
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embedding
/embed/VBG
|
all individual graphs . In the
|
W15-1303 |
relies on the notion of semantic
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embedding
/embedding/NN
|
and a fine-grained classification
|
J89-1005 |
English that contains left and right
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embedding
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|
and finite central embedding
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N06-4008 |
makes mistakes ) . Ndaona includes
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embedding
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|
and graphics parameter estimation
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N12-1049 |
rules , the possibility of phrasal
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embedding
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|
and modification in time expressions
|
W10-4104 |
PClauses indicating the depth of the
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embedding
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and overlapping . PClause " fTfT
|