| P15-1107 | that hierarchically builds an | embedding | for a paragraph from embeddings |
| P15-1104 | the supervised data to find an | embedding | subspace that fits the task complexity |
| C88-1033 | reformulated as the problem of finding an | embedding | function f from the representational |
| P98-2242 | given , which are realised in an | embedding | algorithm . The significant aspect |
| W03-2200 | embedding discussed here is an | embedding | or an enrichment of MT by combining |
| P15-2048 | labels . Specifically , we learn an | embedding | for each label and each feature |
| P13-2087 | labeled data , and produces an | embedding | in the same space , but with |
| P15-1167 | Abstract This paper proposes an | embedding | matching approach to Chinese |
| D15-1232 | words , where we assume that an | embedding | of each word can represent its |
| J13-1008 | complex syntactic patterns , and | embedding | useful morphological features |
| S15-2085 | , word prior polarities , and | embedding | clusters . Using weighted Support |
| D14-1113 | word sense discrimination and | embedding | learning , by non-parametrically |
| J92-4004 | Grammars ( TAG ) in such a manner and | embedding | it in a unification-based framework |
| P98-1097 | is to exploit term overlap and | embedding | so as to yield a substantial |
| P15-1049 | discover the power of statistical and | embedding | features . However , tree-based |
| J88-2001 | event-related information from text and | embedding | those methods in question-answering |
| D15-1153 | way to combine traditional and | embedding | features compared with previous |
| W10-4104 | other examples of this section are | embedding | , while this example is of overlapping |
| W15-3822 | method called Surrounding based | embedding | feature ( SBE ) , and two newly |
| W15-3822 | : Left-Right surrounding based | embedding | feature ( LR_SBE ) and MAX surrounding |
