P15-1009 |
semantic information and enforce the
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embedding
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space to be semantically smooth
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P15-2058 |
first discover semantic cliques in
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embedding
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spaces by a fast clustering algorithm
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W96-0413 |
set for the same variable in the
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embedding
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structure . An inner quantifier
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W06-0603 |
marker presence and syntactic
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embedding
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structure to be strongly associated
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P15-1104 |
the supervised data to find an
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embedding
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subspace that fits the task complexity
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W06-2205 |
generated from a text corpus by
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embedding
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syntactically parsed sentences
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P15-1009 |
regularization terms to constrain the
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embedding
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task . We empirically evaluate
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D15-1054 |
successful application of word
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embedding
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techniques for the task of click
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D15-1036 |
evaluation methods for unsupervised
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embedding
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techniques that obtain meaningful
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D15-1054 |
is to explore the use of word
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embedding
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techniques to generate effective
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C86-1088 |
. The analysis is completed by
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embedding
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the DRS representing the text
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J10-3010 |
extrinsic evaluation is done by
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embedding
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the expansion systems into a
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P07-2028 |
metaphor . We also show a way of
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embedding
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the invariant mappings in a semantic
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D08-1086 |
investigated , and then a LVCSR system
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embedding
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the presented analyzer is evaluated
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W14-1411 |
for the adjectival challenge by
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embedding
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the record types defined to deal
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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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P15-1107 |
and words , then decodes this
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embedding
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to reconstruct the original paragraph
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W13-3213 |
or not , by classifying its RNN
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embedding
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together with those of its siblings
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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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J13-1008 |
complex syntactic patterns , and
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embedding
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useful morphological features
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