Predicate symbols

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  • The internal structure of the locative predicate-complement form-class in German is described within the framework of a generative grammar consisting of a phrase-structure (PS) component, a semantic (S) component, and a transformation (T) component. The S-component is interposed between the PS-component and the T-component.

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  • Symbolic execution builds predicates that characterize the conditions under which execution paths can be taken and the effect of the execution on program state. Extracting predicates through symbolic execution is the essential bridge from the complexity of program behavior to the simpler and more orderly world of logic. This chapter presents the symbolic execution and proof of properties.

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  • What do the ¯elds of astronomy, economics, ¯nance, law, mathematics, med- icine, physics, and sociology have in common? Not much in the way of sub- ject matter, that's for sure. And not all that much in the way of methodology. What they do have in common, with each other and with many other ¯elds, is their dependence on a certain standard of rationality.

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  • This chapter introduces analytic combinatorics, a modern approach to the study of combinatorial structures of the sort that we encounter frequently in the analysis of algorithms. The approach is predicated on the idea that combinatorial structures are typically defined by simple formal rules that are the key to learning their properties.

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  • Symbolic execution builds predicates that characterize the conditions under which execution paths can be taken and the effect of the execution on program state. Extracting predicates through symbolic execution is the essential bridge from the complexity of program behavior to the simpler and more orderly world of logic. This chapter presents the symbolic execution and proof of properties.

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  • The LOGON MT demonstrator assembles independently valuable general-purpose NLP components into a machine translation pipeline that capitalizes on output quality. The demonstrator embodies an interesting combination of hand-built, symbolic resources and stochastic processes. h1 , { h1 :proposition m(h3 ), h4 :proper q(x5 , h6 , h7 ), h8 :named(x5,‘Bodø’), h9 : populate v(e2 , , x5 ), h9 : densely r(e2 ) }, { h 3 =q h9 , h6 =q h8 } Figure 1: Simplified MRS representation for the utterance ‘Bodø is densely populated.

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