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Model Building for Natural Language Understanding

This paper introduces a discipline in the field of automated reasoning that has received too little attention so far within computational semantics: model generationor model buildingfor first-order logic. Model builders offer a positive handle on the satisfiability problem (theorem provers are, in general, unable to detect the satisfiability of a problem) and are therefore often used in tandem with theorem provers (who offer a negative handle on satisfiability). But a model builder, as its name suggests, has another attractive property: it is able to construct a minimal model for a problem. As it turns out, the representations of these models are not only flat and extremely easy to process, they also embody the information required for many natural language understanding tasks. Although it is certainly true that model building hasn’t reach a state of matureness that automated theorem proving has achieved in the last decades, it is also fair to say that performances in model