P15-1009 semantic information and enforce the embedding space to be semantically smooth
P15-2058 first discover semantic cliques in embedding spaces by a fast clustering algorithm
W96-0413 set for the same variable in the embedding structure . An inner quantifier
W06-0603 marker presence and syntactic embedding structure to be strongly associated
P15-1104 the supervised data to find an embedding subspace that fits the task complexity
W06-2205 generated from a text corpus by embedding syntactically parsed sentences
P15-1009 regularization terms to constrain the embedding task . We empirically evaluate
D15-1054 successful application of word embedding techniques for the task of click
D15-1036 evaluation methods for unsupervised embedding techniques that obtain meaningful
D15-1054 is to explore the use of word embedding techniques to generate effective
C86-1088 . The analysis is completed by embedding the DRS representing the text
J10-3010 extrinsic evaluation is done by embedding the expansion systems into a
P07-2028 metaphor . We also show a way of embedding the invariant mappings in a semantic
D08-1086 investigated , and then a LVCSR system embedding the presented analyzer is evaluated
W14-1411 for the adjectival challenge by embedding the record types defined to deal
J88-2001 event-related information from text and embedding those methods in question-answering
P15-1107 and words , then decodes this embedding to reconstruct the original paragraph
W13-3213 or not , by classifying its RNN embedding together with those of its siblings
J03-3003 we employed and various ways of embedding translation into a retrieval
J13-1008 complex syntactic patterns , and embedding useful morphological features
hide detail