C80-1074 of other predicate function by embedding operator fIII . Connecting operator
C86-1008 axiomatic theory of dialogue , embedding rhetorical patterns , focusing
C86-1088 The analysis is completed by embedding the DRS representing the text
D08-1086 investigated , and then a LVCSR system embedding the presented analyzer is evaluated
D14-1062 relevance for the in-domain task . By embedding our latent domain phrase model
D14-1167 propose a novel method of jointly embedding entities and words into the same
D14-1167 Times corpus show that jointly embedding brings promising improvement
D14-1167 facts , compared to separately embedding knowledge graphs and text . Particularly
D14-1167 text . Particularly , jointly embedding enables the prediction of facts
D14-1167 reasoning task show that jointly embedding is comparable to or slightly
D15-1031 study the problem of jointly embedding a knowledge base and a text corpus
D15-1038 completion impute missing facts by embedding knowledge graphs in vector spaces
D15-1200 relat - edness , controlling for embedding dimen - sionality . We find that
D15-1246 syntactic features . We show that all embedding approaches behave similarly in
E09-3009 Vector Space Model ( VSM ) by embedding additional types of information
H89-2024 to be added to a document by embedding user-defined sequences of text
H90-1008 predicates to be at about the time of embedding event ( e.g. , the assertion
H90-1008 for that causes re-traversal of embedding links , the a and b episodes
J03-3003 employed and various ways of embedding translation into a retrieval
J10-3010 extrinsic evaluation is done by embedding the expansion systems into a
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