Skip to main content

Abstract

The integration of language and vision capabilities in computers can be seen purely as a multi-media task without any theoretical assumptions being required. However, it is worth exploring whether the modalities have anything serious in common, in particular in the light of claim that most non-technical language use is metaphorical. What consequences would that have for the underlying relationship of language and vision: is it possible that vision is largely metaphorical?

The conclusion (see also, Wilks 1978b and Wilks and Okada (in press) is that visual processing can embody structural ambiguity (whether compositional or not), but not anything analogous to metaphor. Metaphor is essentially connected with the extension of sense and only symbols can have senses. But if it makes no sense to say a figure can be metaphorical (unless it embodies symbolic elements) that must also mean, alas, that it makes no sense to say it is literally anything either. Only a symbol can be literally something. A hat is a hat is a hat, but never, ever literally so.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Anderson, J. (1971). The Grammar of Case. Cambridge U.P.: Cambridge.

    Google Scholar 

  • Arens, Y. (1981). Using Language and Context in the Analysis of Text. In Proceedings of Internat. Joint Conf. on Artificial Intelligence. Tbilisi. 1121–1129.

    Google Scholar 

  • Colby, K. (1973). The Simulation of Belief Systems. In Schank, R. & Colby, K. (eds.) Computer Models of Thought and Language, 251–286. W.H. Freeman: San Francisco.

    Google Scholar 

  • Church, K. & Hanks, P. (1989). Word Association Norms, Mutual Information and Lexicography. In Proceedings of The 27th Meeting of ACM, 45–51. Vancouver, BC.

    Google Scholar 

  • Clowes, M. (1972). Scene Analysis and Picture Grammars. In Nake, K. & Rosenfeld, A. (eds.) Graphic Languages, 107–127. N. Holland: Amsterdam.

    Google Scholar 

  • Dailey, D. P. (1986). The Extraction of a Minimum Set of Semantic Primitives from a Monolingual Dictionary is NP-Complete. Computational Linguistics 12(4): 306–307.

    Google Scholar 

  • Fass, D. (1986). Collative Semantics. In Proceedings of The Internat. Conf. on Computational Linguistics, 341–343.

    Google Scholar 

  • Fillmore, C. (1977). Scenes and Frames Semantics. In Zampolli, A. (ed.) Linguistic Structures Processing. N. Holland: Amsterdam.

    Google Scholar 

  • Gazdar, G. (1990). An Introduction to DATR. In Evans, R. & Gazdar, G. (eds.) The DATR Papers. Cognitive Science Research Papers CSRP 139, School of Cognitive and Computing Sciences, University of Sussex.

    Google Scholar 

  • Gregory, R. (1970). The Grammar of Vision. The Listener, BBC: London.

    Google Scholar 

  • Guo, C. M. (1994). Machine Tractable Dictionaries, Ablex: Norwood, NJ.

    Google Scholar 

  • Guthrie, L., Slator, B., Wilks, Y. & Bruce, R. (1990). Is There Content in Empty Heads? In Proceedings of The Internat. Conf. on Computational Linguistics, 236–240. Helsinki, Finland.

    Google Scholar 

  • Hanks, P. (1986). Typicality and Meaning Potentials. In Proceedings of The European Conf. on Lexicography, 213–223. Zurich, Switzerland.

    Google Scholar 

  • Hornby, A. (1963). The Advanced Learner’s Dictionary of English. O.U.P.: Oxford.

    Google Scholar 

  • Jackendoff, R. (1975). A System of Semantic Primitives. In Schank & Nash-Webber (eds.) Theoretical Issues in Natural Language Processing, 112–117. BBN: Cambridge, MA.

    Google Scholar 

  • Johnson-Laird, P. (1984). Semantic Primitives or Meaning Postulates: Mental Models of Propositional Representations. In Bara, B. & Guida, G. (eds.), Computational Models of Natural Language Processing, 227–246. North-Holland: Amsterdam.

    Google Scholar 

  • Katz, J. & Fodor, J. (1963). The Structure of a Semantic Theory. Language; pp. 334–349.

    Google Scholar 

  • Katz, J. (1972). Semantic Theory. Harper & Row: New York.

    Google Scholar 

  • Kay, M. (1989). The Concrete Lexicon and the Abstract Dictionary. In Proceeding of The Fifth Annual Conference of UW Centre for the New Oxford English Dictionary, 54–64. Oxford,England.

    Google Scholar 

  • Martin, J. (1990). A Computational Model of Metaphor Interpretation. Academic Press: New York.

    MATH  Google Scholar 

  • Michotte, A. (1954). La perception de la causalite. Studia Psychologica: Publications Universitaires de Louvain.

    Google Scholar 

  • Minsky, M. (1975). Frame Systems. In Schank & Nash-Webber (eds.) Theoretical Issues in Natural Language Processing, 89–94. BBN: Cambridge, MA.

    Google Scholar 

  • Newell, A. (1973). Artificial Intelligence and the Concept of Mind. In Schank, R. & Colby, K. (eds.) Computer Models of Thought and Language, 1–60. Freeman: San Francisco.

    Google Scholar 

  • Procter, P. (1978). Longman Dictionary of Contemporary English. Longman: London.

    Google Scholar 

  • Pustejovsky, J. & Bergler, S. (1987). The Acquisition of Conceptual Structure for the Lexicon. In Proceedings of The Amer. Conf. on Artificial Intelligence, 566–576.

    Google Scholar 

  • Rieger, C. (1976). Computers and Thought Lecture at IJCAI4. Artificial Intelligence, 88–98.

    Google Scholar 

  • Ryle, G. (1949). The Concept of Mind. Hutchinson: London.

    Google Scholar 

  • Schank, R. (1973). Identification of Conceptualizations underlying Natural Language. In Schank, R. & Colby, K. (eds.) Computer Models of Thought and Language, 187–248. Freeman: San Francisco.

    Google Scholar 

  • Searle, J. (1979). Literal Meaning. In Expression and Meaning. Cambridge University Press: Cambridge.

    Google Scholar 

  • Searle, J. (1983). Intentionality: An Essay in the Philosophy of Mind. Cambridge University Press: Cambridge.

    Google Scholar 

  • Simon, H. (1969). The Architecture of Complexity. In The Sciences of the Artificial. MIT Press: Cambridge, MA.

    Google Scholar 

  • Sinclair, J. (ed.) (1987) Cobuild Dictionary of the English Language. Collins: London.

    Google Scholar 

  • Tooke, H. (1769). The Diversions of Pur ley. London.

    Google Scholar 

  • Whorf, B. (1956). Language, Thought and Reality. MIT Press: Cambridge, MA.

    Google Scholar 

  • Wilensky, R. (1987). Some Complexities of Goal Analysis. In Proceedings of The Third Conf. on Theoret. Issues in Language Processing, 97–99.

    Google Scholar 

  • Wilks, Y. (1972). Grammar, Meaning and The Machine Analysis of Language. Routledge: London & Boston, MA.

    Google Scholar 

  • Wilks, Y. (1975). Preference Semantics’. In Keenan, E. (ed.), Formal Semantics of Natural Language, 78–88. Cambridge U.P.: Cambridge.

    Google Scholar 

  • Wilks, Y. (1975a). An Intelligent Analyzer and Understander of Natural Language. Comm. A.C.M., 112–119.

    Google Scholar 

  • Wilks, Y. (1975b). A Preferential, Pattern-Matching Semantics for Natural Language. Artificial Intelligence, 76–88.

    Google Scholar 

  • Wilks, Y. (1977). Good and Bad Arguments for Semantic Primitives. Communication and Cognition, 187–197.

    Google Scholar 

  • Wilks, Y. (1978). Making Preferences More Active. Artificial Intelligence 11: 243–263.

    Article  Google Scholar 

  • Wilks, Y. (1978). Semantic Primitives in Language and Vision. In Proceedings of The Second Conf. on Theoretical Issues in Natural Language Processing. Champaign-Urbana, IL, 67–72.

    Google Scholar 

  • Wilks, Y., Fass, D., Guo, C-M., McDonald, J., Plate, T. & Slator, B. (1990). Providing Machine Tractable Dictionary Tools. Journal of Machine Translation 5(2): 99–151.

    Article  Google Scholar 

  • Wilks, Y. & Fass, D. (1992). The Preference Semantics Family. Comput. Math. Applic. 23: 99–117.

    Article  Google Scholar 

  • Wilks, Y., Slator, B. & Guthrie, L. (in press). Electric Words. MIT Press: Cambridge, MA.

    Google Scholar 

  • Wilks, Y. & Okada, N. (eds.). (in press) Computer Language and Vision Across the Pacific. Ablex: Norwood, NJ.

    Google Scholar 

  • Wittengstein, L. (1953). Philosophical Investigations. Blackwell: Oxford.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Kluwer Academic Publishers

About this chapter

Cite this chapter

Wilks, Y. (1995). Language, Vision and Metaphor. In: Mc Kevitt, P. (eds) Integration of Natural Language and Vision Processing. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-1639-5_4

Download citation

  • DOI: https://doi.org/10.1007/978-94-009-1639-5_4

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-0-7923-3944-1

  • Online ISBN: 978-94-009-1639-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics