| 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 |
| 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 |
| D14-1015 | investigate how to improve bilingual | embedding | which has been successfully used |
| D14-1062 | relevance for the in-domain task . By | embedding | our latent domain phrase model |
| E09-3009 | Vector Space Model ( VSM ) by | embedding | additional types of information |
| W14-1411 | for the adjectival challenge by | embedding | the record types defined to deal |
| C86-1088 | . The analysis is completed by | embedding | the DRS representing the text |
| W06-2205 | generated from a text corpus by | embedding | syntactically parsed sentences |
| H89-2024 | informarion to be added to a document by | embedding | user-defined sequences of text |
| J10-3010 | extrinsic evaluation is done by | embedding | the expansion systems into a |
| W10-2802 | ularities . This latter is employed by | embedding | prior FrameNet-derived knowledge |
