E09-3009 |
Vector Space Model ( VSM ) by
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embedding
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additional types of information
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P98-2242 |
given , which are realised in an
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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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algorithms and research on them
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P98-1016 |
of the clustering is a large CG
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all individual graphs . In the
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W15-1303 |
relies on the notion of semantic
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and a fine-grained classification
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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
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D15-1029 |
dot product between each word
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embedding
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and part of the first hidden
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W15-2619 |
rank synonym candidates with word
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embedding
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and pseudo-relevance feedback
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D14-1167 |
Zheng Abstract We examine the
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embedding
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approach to reason new relational
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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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D15-1305 |
, it is often recognized that
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embedding
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based on distributional hypothesis
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T75-2004 |
objects . A small grammar rich in
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embedding
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capabilities is coded in Woods
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D10-1116 |
thus maintaining a reasonable
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capacity . 1 Introduction Steganography
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P84-1085 |
a dominant constituent of the
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clause . Also in this case ,
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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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D12-1086 |
based on Euclidean co-occurrence
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embedding
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combines the paradigmatic context
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P14-1138 |
penalty function that ensures word
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embedding
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consistency across two directional
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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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