Natural Language Processing Using Propositional Semantic Networks. Minds and Machines

We describe a knowledge representation and inference formalism, based on an intensional propositional semantic network, in which variables are structured terms consisting of quanti-er, type, and other information. This has three important consequences for natural language processing. First, this leads to an extended, more \natural " formalism whose use and rep-resentations are consistent with the use of variables in natural language in two ways: the structure of representations mirrors the structure of the language and allows re-use phenom-ena such as pronouns and ellipsis. Second, the formalism allows the specication of description subsumption as a partial ordering on related concepts (variable nodes in a semantic network) that relates more general concepts to more specic instances of that concept, as is done in language. Finally, this structured variable representation simplies the resolution of some rep-resentational diculties with certain classes of natural language sentences, namely, donkey sentences and sentences involving branching quantiers. The implementation of this formal-ism is called ANALOG (A NAtural Logic) and its utility for natural language processing tasks is illustrated.