| C90-2071 | considerations arising from the | embedding /embedding/NN | phrase and context . Figure 1 |
| C90-3012 | on various types of syntactic | embedding /embedding/NN | constructions in Germanic languages |
| C92-2072 | thus , we would include multiple | embedding /embedding/NN | constructions , poten - ACT , |
| C92-2095 | eliminate unnecessary center - | embedding /embedding/NN | ; and ( 3 ) eliminating of scrambling |
| C92-3137 | re-Evaluation of the attitude in the | embedding /embedding/NN | attitude contexts . Thus , ill |
| C96-1045 | indirectly in terms of a homomorphic | embedding /embedding/NN | into Quasi Logical Form ( QLF |
| D08-1086 | investigated , and then a LVCSR system | embedding /embed/VBG | the presented analyzer is evaluated |
| D10-1116 | thus maintaining a reasonable | embedding /embedding/NN | capacity . 1 Introduction Steganography |
| D12-1086 | based on Euclidean co-occurrence | embedding /embedding/NN | combines the paradigmatic context |
| D14-1012 | effectively incorporating the word | embedding /embedding/NN | features within the framework |
| D14-1012 | prototype approach , for utilizing the | embedding /embedding/NN | features . The presented approaches |
| D14-1012 | approaches can better utilize the word | embedding /embedding/NN | features , among which the distributional |
| D14-1012 | outperforming the dense and continuous | embedding /embedding/NN | features by nearly 2 points of |
| D14-1015 | investigate how to improve bilingual | embedding /embedding/NN | which has been successfully used |
| D14-1015 | translation ( SMT ) . Despite bilingual | embedding /embedding/NN | 's success , the contextual information |
| D14-1015 | memory-efficient model for learning bilingual | embedding /embedding/NN | , taking both the source phrase |
| D14-1015 | generated from our proposed bilingual | embedding /embedding/NN | model are used as features in |
| D14-1030 | some learned representation ( | embedding /embedding/NN | ) , and ( 3 ) compute output |
| D14-1030 | Sec - ondly , the neural network | embedding /embedding/NN | of word i can predict the MEG |
| D14-1062 | relevance for the in-domain task . By | embedding /embed/VBG | our latent domain phrase model |
