S15-2092 |
hand-crafted features and message-level
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embedding
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fea - tures , and uses an SVM
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J03-3003 |
we employed and various ways of
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embedding
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translation into a retrieval
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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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P00-1011 |
select from a set of noun phrases ,
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embedding
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proper names of different semantic
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W03-0603 |
visually-grounded semantics and their
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embedding
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in a compositional parsing frame
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W15-3814 |
literature . Recent advances in word
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embedding
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make computation of word distribution
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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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S15-2085 |
, word prior polarities , and
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embedding
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clusters . Using weighted Support
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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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P84-1007 |
manifests first-degree center
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embedding
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of the category S * , as a result
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T87-1012 |
they all assiduously avoid center
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embedding
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in favor of strongly left - or
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D15-1252 |
, penalizing embeddings , re -
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embedding
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words , and dropout . We also
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S14-2033 |
concatenating the sentiment-specific word
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embedding
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( SSWE ) features with the state-of-the-art
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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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P13-1078 |
translation model . In addition , word
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embedding
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is employed as the input to the
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J14-2006 |
and Bob must find some way for
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embedding
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hidden information into their
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J88-2001 |
event-related information from text and
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embedding
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those methods in question-answering
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D14-1012 |
effectively incorporating the word
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embedding
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features within the framework
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D15-1183 |
Chunyan Abstract Most existing word
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embedding
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methods can be categorized into
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W00-0507 |
Abstract This paper describes the
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embedding
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of a statistical translation
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