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On the referential competence of some machines

On the referential competence of some machines 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

On the referential competence of some machines

Artificial Intelligence Review , Volume 10 (2) – Jun 24, 2004

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References (38)

Publisher
Springer Journals
Copyright
Copyright
Subject
Computer Science; Artificial Intelligence; Computer Science, general
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/BF00159213
Publisher site
See Article on Publisher Site

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.

Journal

Artificial Intelligence ReviewSpringer Journals

Published: Jun 24, 2004

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