Abstract
Various approaches to computational metaphor interpretation are based on pre-existing similarities between source and target domains and/or are based on metaphors already observed to be prevalent in the language. This paper addresses similarity-creating cross-modal metaphoric expressions. It is shown how the “abstract concept as object” (or reification) metaphor plays a central role in a large class of metaphoric extensions. The described approach depends on the imposition of abstract ontological components, which represent source concepts, onto target concepts. The challenge of such a system is to represent both denotative and connotative components which are extensible, together with a framework of general domains between which such extensions can conceivably occur. An existing ontology of this kind, consistent with some mathematic concepts and widely held linguistic notions, is outlined. It is suggested that the use of such an abstract representation system is well adapted to the interpretation of both conventional and unconventional metaphor that is similarity-creating.
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References
Aarts, J., & Calbert, J. (1979). Metaphor and non-metaphor: The semantics of adjective-noun combinations. Tübingen: Max Niemayer.
Agerri, R., Barnden, J., Lee, M., & Wallington, A. (2007). Invariant mappings and contexts in a computational approach to metaphor interpretation. In IJCAI-MRCS.
Barnden, J., Glasbey, S., Lee, M., & Wallington, A. M. (2003). Domain-transcending mappings in a system for metaphorical reasoning. In EACL (pp. 57–61).
Barnden, J., Glasbey, S., Lee, M., & Wallington, A. M. (2004). Varieties and directions of inter-domain influence in metaphor. Metaphor and Symbol, 19, 1–30.
Bouchard, D. (1995). The semantics of syntax: A minimalist approach to grammar. Chicago: University of Chicago Press.
Brugman, C., & Lakoff, G. (1988). Cognitive typology and lexical networks. In S. Small, G. Cottrell, & M. Tanenhaus (Eds.), Lexical ambiguity resolution. San Mateo: Morgan Kaufmann.
Carbonell, J. (1980). Metaphor: A key to extensible semantic analysis. In ACL (pp. 17–21).
Carbonell, J. (1982). Metaphor: An inescapable phenomenon in natural-language comprehension. In W. Lehnert & M. Ringle (Eds.), Strategies for natural language processing (pp. 415–434). Hillsdale: Erlbaum.
Carbonell, J., & Minton, S. (1983). Metaphor and common-sense reasoning (Rep. No. CMU-CS-83-110). Carnegie-Mellon University, Pittsburgh.
Cummins, D., Reusser, K., Kintsch, W., & Weimer, R. (1988). The role of understanding in solving word problems. Cognitive Psychology, 20, 405–438.
Fass, D. (1997). Processing metonymy and metaphor. Greenwich: Ablex.
Fass, D., & Wilks, Y. (1983). Preference semantics, ill-formedness, and metaphor. American Journal of Computational Linguistics, 9, 178–187.
Fillmore, C. (1968). The case for case. In E. Bach & R. Harms (Eds.), Universals in linguistic theory (pp. 1–88). New York: Holt, Rinehart and Winston.
Gentner, D., & France, I. (1988). The verb mutability effect: Studies of the combinatorial semantics of nouns and verbs. In S. Small, G. Cottrell, & M. Tanenhaus (Eds.), Lexical ambiguity resolution. San Mateo: Morgan Kaufmann.
Gibson, J. (1977). The theory of affordances. In R. Shaw & J. Bransford (Eds.), Perceiving, acting and knowing (pp. 67–82). Hillsdale: Erlbaum.
Gruber, J. (1965). Studies in lexical relations. Doctoral Dissertation, MIT, Cambridge, MA. Indiana University Linguistics Club, Bloomington, IN.
Hobbs, J. (1992). Metaphor and abduction. In A. Ortony, J. Slack, & O. Stock (Eds.), Communication from an artificial intelligence perspective: Theoretical and applied issues (pp. 35–58). Berlin: Springer.
Indurkhya, B. (1992). Metaphor and cognition. Dordrecht: Kluwer.
Jackendoff, R. (1983). Semantics and cognition. Cambridge: MIT Press.
Kaput, J. (1989). Representation systems and mathematics. In C. Janvier (Ed.), Problems of representation in the teaching and learning of mathematics (pp. 19–26). Hillsdale: Erlbaum.
Lakoff, G., & Johnson, M. (1980). Metaphors we live by. Chicago: Chicago University Press.
Lakoff, G., & Nuñez, R. (2000). Where does mathematics come from? How the embodied mind brings mathematics into being. New York: Basic Books.
LeBlanc, M., & Weber-Russell, S. (1996). A computer model of the role of text integration in the solution of arithmetic word problems. Cognitive Science, 20, 357–408.
Martin, J. (1990). A computational model of metaphor interpretation. New York: Academic Press.
Narayanan, S. (1999). Moving right along: A computational model of metaphoric reasoning about events. In AAAI.
Osgood, C. (1980). The cognitive dynamics of synesthesia and metaphor. In R. Honeck & R. Hoffman (Eds.), Cognition and figurative language (pp. 203–238). Hillsdale: Erlbaum.
Quine, W. (1969). Ontological relativity and other essays (The John Dewey essays in philosophy). New York: Columbia University Press.
Russell, S. W. (1976). Computer understanding of metaphorically used verbs. American Journal of Computational Linguistics, Microfiche 44.
Russell, S. W. (1986). Information and experience in metaphor: A perspective from computer analysis. Metaphor and Symbolic Activity, 1, 227–270.
Russell, S. W. (1989). Verbal concepts as abstract structures: The most basic conceptual metaphor? Metaphor and Symbolic Activity, 4, 55–60.
Russell, S. W. (1992). Metaphoric coherence: Distinguishing verbal metaphor from anomaly. Computational Intelligence, 8, 553–574.
Schank, R. (1975). Conceptual information processing. Amsterdam: North-Holland.
Suwa, M., & Motoda, H. (1991). Learning metaphorical relationships between concepts based on semantic representation using abstract primitives. In IJCAI-CANL (pp. 123–131).
Whorf, B. (1956). Language and logic. In J. B. Carroll (Ed.), Language, thought, and reality: Selected papers of Benjamin Lee Whorf (pp. 233–245). Cambridge: MIT Press.
Wilks, Y. (1977). Knowledge structures and language boundaries. In IJCAI (pp. 151–157).
Wilks, Y. (1978). Making preferences more active. Artificial Intelligence, 11, 197–223.
Wilks, Y. (2007). Ontotherapy: Or, how to stop worrying about what there is. In NLPCS (pp. 3–22).
Winston, P. (1978). Learning by creating and justifying transfer frames. Artificial Intelligence, 10, 147–172.
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An earlier version of this paper was presented at the 5th International Workshop on Natural Language Processing and Cognitive Science—NLPCS 2008.
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Weber Russell, S. Abstraction as a basis for the computational interpretation of creative cross-modal metaphor. Int J Speech Technol 11, 125 (2008). https://doi.org/10.1007/s10772-009-9042-8
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DOI: https://doi.org/10.1007/s10772-009-9042-8