| other,18-6-P95-1034,ak | generators </term> and enhance their <term> | portability | </term> , even when perfect <term> knowledge | #28048 We also discuss how the hybrid generation model can be used to simplify current generators and enhance theirportability, even when perfect knowledge is in principle obtainable. | |
| tech,9-4-P95-1034,ak | attack these problems , we have built a <term> | hybrid generator | </term> , in which gaps in <term> symbolic | #28007 To attack these problems, we have built ahybrid generator, in which gaps in symbolic knowledge are filled by statistical methods. | |
| other,11-1-P95-1034,ak | the integration of vast amounts of <term> | knowledge | </term> : lexical , grammatical , and conceptual | #27961 Large-scale natural language generation requires the integration of vast amounts ofknowledge: lexical, grammatical, and conceptual. | |
| other,6-3-P95-1034,ak | missing . It must also be robust against <term> | incomplete or inaccurate inputs | </term> . To attack these problems , we have | #27993 It must also be robust againstincomplete or inaccurate inputs. | |
| tech,21-4-P95-1034,ak | symbolic knowledge </term> are filled by <term> | statistical methods | </term> . We describe <term> algorithms </term> | #28019 To attack these problems, we have built a hybrid generator, in which gaps in symbolic knowledge are filled bystatistical methods. | |
| tech,14-6-P95-1034,ak | </term> can be used to simplify current <term> | generators | </term> and enhance their <term> portability | #28044 We also discuss how the hybrid generation model can be used to simplify currentgenerators and enhance their portability, even when perfect knowledge is in principle obtainable. | |
| tech,2-5-P95-1034,ak | statistical methods </term> . We describe <term> | algorithms | </term> and show experimental results . We | #28024 We describealgorithms and show experimental results. | |
| other,16-4-P95-1034,ak | generator </term> , in which gaps in <term> | symbolic knowledge | </term> are filled by <term> statistical methods | #28014 To attack these problems, we have built a hybrid generator, in which gaps insymbolic knowledge are filled by statistical methods. | |
| other,13-2-P95-1034,ak | to operate well even when pieces of <term> | knowledge | </term> are missing . It must also be robust | #27983 A robust generator must be able to operate well even when pieces ofknowledge are missing. | |
| model,5-6-P95-1034,ak | results . We also discuss how the <term> | hybrid generation model | </term> can be used to simplify current <term> | #28035 We also discuss how thehybrid generation model can be used to simplify current generators and enhance their portability, even when perfect knowledge is in principle obtainable. | |
| other,23-6-P95-1034,ak | portability </term> , even when perfect <term> | knowledge | </term> is in principle obtainable . Aggregating | #28053 We also discuss how the hybrid generation model can be used to simplify current generators and enhance their portability, even when perfectknowledge is in principle obtainable. | |
| tech,1-2-P95-1034,ak | , grammatical , and conceptual . A <term> | robust generator | </term> must be able to operate well even | #27971 Arobust generator must be able to operate well even when pieces of knowledge are missing. | |
| tech,0-1-P95-1034,ak | improvement </term> over the original tests . <term> | Large-scale natural language generation | </term> requires the integration of vast | #27950 The most sophisticated complex learning method offers a small, but statistically significant, improvement over the original tests.Large-scale natural language generation requires the integration of vast amounts of knowledge: lexical, grammatical, and conceptual. |
