D15-1054 is to explore the use of word embedding techniques to generate effective
K15-1009 evaluation of four popular word embedding methods in the context of four
S14-2033 concatenating the sentiment-specific word embedding ( SSWE ) features with the state-of-the-art
W15-1501 on the popular skip-gram word embedding model . The novelty of our approach
S15-2155 work on using vector space word embedding models for hypernym-hyponym extraction
D15-1196 outperforms the state-of-the-art word embedding methods in both representation
D14-1012 effectively incorporating the word embedding features within the framework
W15-2608 based approach that uses word embedding features to recognize drug names
W15-2619 rank synonym candidates with word embedding and pseudo-relevance feedback
W15-3041 and a feature produced with word embedding models ( SHEF - QuEst + + ) .
hide detail