Abstract
The main reason why systems of natural language understanding are often said not to “really” understand natural language is their lack of referential competence. A traditional system, even an ideal one, cannot relate language to the perceived world, whereas — obviously — a human speaker can. The paper argues that the recognition abilities underlying the application of language to the world are indeed a prerequisite of semantic competence.
If a system of the traditional kind were appropriately endowed with the analytic abilities of a system of artificial vision, it would display (partial) referential competence: e.g. it would be able to verify sentences. In response to Searle’s objections to the so-called “robot reply”, the paper argues that such an integrated system could not be considered as essentially on a par with a purely inferential system of the traditional kind, unless one were prepared to regard even the human understanding system as “purely syntactic” (and therefore incapable of genuine understanding).
Research leading to this article was partly suppor ted by the Italian MURST - 40% funds.
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© 1996 Kluwer Academic Publishers
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Marconi, D. (1996). On the Referential Competence of Some Machines. In: Mc Kevitt, P. (eds) Integration of Natural Language and Vision Processing. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-1639-5_10
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DOI: https://doi.org/10.1007/978-94-009-1639-5_10
Publisher Name: Springer, Dordrecht
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