| 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 |
| P14-1146 | learning sentiment - specific word | embedding | ( SSWE ) , which encodes sentiment |
| D15-1196 | outperforms the state-of-the-art word | embedding | methods in both representation |
| W15-3820 | performance of two state-of-the-art word | embedding | methods , namely word2vec and |
| D14-1012 | effectively incorporating the word | embedding | features within the framework |
| D15-1183 | writing styles , into the word | embedding | model . Since generative models |
| D14-1012 | approaches can better utilize the word | embedding | features , among which the distributional |
| 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 + + ) . |
