Titlebook: Natural Language Generation; New Results in Artif Gerard Kempen Book 1987 Martinus Nijhoff Publishers, Dordrecht 1987 artificial intelligen

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乳汁

Paul S. Jacobslement it on OS kernel or hypervisor level. However, if the system is attacked by the ring 0 or ring 1 level risks, the OS kernel or hypervisor is unable to provide the trusted base, which may cause an incorrect result. To solve the shortcomings, we choose the System Management Mode (SMM) to build a

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lement it on OS kernel or hypervisor level. However, if the system is attacked by the ring 0 or ring 1 level risks, the OS kernel or hypervisor is unable to provide the trusted base, which may cause an incorrect result. To solve the shortcomings, we choose the System Management Mode (SMM) to build a

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Monocle

Factors Contributing to Efficiency in Natural Language Generationr natural language generation systems, there are nevertheless many points of correspondence among them. To organize these points, we introduce an abstract reference model for the generation process. We then identify five specific factors with respect to that model that certain designs incorporate to make them more efficient computationally.

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A French and English Syntactic Component for Generation) is domain-independent. Thus, it is of the utmost interest that a syntactic component applying such rules be developed ’once and for all’. A syntactic component will be described for both French and English.

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Natural Language Generation978-94-009-3645-4Series ISSN 0168-132X

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0168-132X Overview: 978-94-010-8131-3978-94-009-3645-4Series ISSN 0168-132X

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NATO Science Series E:http://image.papertrans.cn/n/image/661788.jpg