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Theoria et Historia Scientiarum

An Artificial Intelligence Approach to Metaphor Understanding
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An Artificial Intelligence Approach to Metaphor Understanding

Authors

  • John A. Barnden
  • Mark G. Lee

DOI:

https://doi.org/10.12775/ths.2002.017

Keywords

metaphor, ATT-Meta system, artificial intelligence, metaphorical reasoning

Abstract

An implemented system called ATT-Meta is sketched. The system can perform an important type of metaphor-based reasoning. It is based on an emphasis on source-domain reasoning and a de-emphasis of the idea of creating new mappings between source domain and target domain. The metaphor-based reasoning is fully integrated into a general framework for uncertain reasoning. The system thereby copes with various different types of uncertainty involved in metaphor understanding. The view of metaphor on which the system is founded is highly understander-relative, and does not require prior assumptions about what literal discourse is.

References

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Bamden, J. A. & Lee, M. G. (1999). An implemented context system that combines belief reasoning, metaphor-based reasoning and uncertainty handling. In P. Bouquet, P. Brezillon & L. Serafini (Eds), Second International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT’99, Trento, Sep 9-11, 1999), Lecture Notes in Artificial Intelligence, 1688, pp. 28-41, Springer.

Falkenhainer, B., Forhus, K. D. & Gentner, D. (1989). The Structure-Mapping Engine: algorithm and examples. Artificial Intelligence, 41 (1), 1-63.

Fass, D. ( 1997). Procesing metaphor and metonymy. Greenwich, Connecticut: Ablex. Goatly, A. (1997). The language of metaphors. London and New York: Routledge.

Hobbs, J. R. (1990). Literature and cognition. CSLI Lecture Notes, No. 21, Center for the Study of Language and Information, Stanford University.

Lakoff, G. (1986). The meanings of literal. Metaphor and Symbolic Activity, 1 (4), pp. 29-36.

Lakoff, G. ( 1993). The contemporary theory of metaphor. In A. Ortony (Ed.), Metaphor and Thought, 2nd edition, pp. 202-251. Cambridge, U.K.: Cambridge University Press.

Martin, J. H. (1990). A computational model of metaphor interpretation. Academic Press.

Martin, J. H. (1996). Computational approaches to figurative language. Metaphor and Symbolic Activity, 11, pp. 85-100.

Narayanan, S. ( 1997). KARMA: Knowledge-based action representations for metaphor and aspect. Ph.D. thesis, EECS Department, U. of California, Berkeley, August 1997.

Veale, T. & Keane, M. T. (1994). Belief modelling, intentionality and perlocution in metaphor comprehension. In Procs. Sixteenth Annual Conference of the Cognitive Science Society, pp. 910-915. Hillsdale, N. J.: Lawrence Erlbaum.

Theoria et Historia Scientiarum

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Published

2007-01-02

How to Cite

1.
BARNDEN, John A. and LEE, Mark G. An Artificial Intelligence Approach to Metaphor Understanding. Theoria et Historia Scientiarum. Online. 2 January 2007. Vol. 6, no. 1, pp. 399-412. [Accessed 2 July 2025]. DOI 10.12775/ths.2002.017.
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Vol. 6 No. 1 (2002): Metaphor: A Multidisciplinary Approach

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