From Conceptual “Mash-ups” to “Bad-ass” Blends: A Robust Computational Model of Conceptual Blending

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Tony Veale: From Conceptual “Mash-ups” to “Bad-ass” Blends: A Robust Computational Model of Conceptual Blending. In: Computational Creativity 2012 ICCC 2012, 1-8.



Conceptual blending is a cognitive phenomenon whose instances range from the humdrum to the pyrotechnical. Most remarkable of all is the ease with which humans regularly understand and produce complex blends. While this facility will doubtless elude our best efforts at computational modeling for some time to come, there are practical forms of conceptual blending that are amenable to computational exploitation right now. In this paper we introduce the notion of a conceptual mash-up, a robust form of blending that allows a computer to creatively re-use and extend its existing common-sense knowledge of a topic. We show also how a repository of such knowledge can be harvested automatically from the web, by targetting the casual questions that we pose to ourselves and to others every day. By acquiring its world knowledge from the questions of others, a computer can eventually learn to pose introspective (and creative) questions of its own.

Extended Abstract


author = {Tony Veale},
title = {From Conceptual “Mash-ups” to “Bad-ass” Blends: A Robust Computational Model of Conceptual Blending},
editor = {Mary Lou Maher, Kristian Hammond, Alison Pease, Rafael Pérez y Pérez, Dan Ventura and Geraint Wiggins},
booktitle = {Proceedings of the Third International Conference on Computational Creativity},
series = {ICCC2012},
year = {2012},
month = {May},
location = {Dublin, Ireland},
pages = {1-8},
url = {,“Mash-ups”_to_“Bad-ass”_Blends:_A_Robust_Computational_Model_of_Conceptual_Blending },
publisher = {International Association for Computational Creativity},
keywords = {computational, creativity},

Used References

Almuhareb, A. and Poesio, M. (2004) Attribute-Based and Value- Based Clustering: An Evaluation. In Proceedings Of EMNLP’2004, pp 158-165.

Barnden, J. A. 2006. Artificial Intelligence, figurative language and cognitive linguistics. G. Kristiansen, M. Achard, R. Dirven, and F. J. Ruiz de Mendoza Ibanez (Eds.), Cognitive Linguistics: Current Application and Future Perspectives, 431-459. Berlin: Mouton de Gruyter.

Brants, T. and Franz, A. 2006. Web 1T 5-gram Version 1. Linguistic Data Consortium.

Budanitsky, A. and Hirst, G. 2006. Evaluating WordNet-based Measures of Lexical Semantic Related- ness. Computational Linguistics, 32(1):13-47.

Falkenhainer, B., Forbus, K. and Gentner, D. 1989. Structure- Mapping Engine: Algorithm and Examples. Artificial Intelligence, 41:1-63.

Gilles Fauconnier and Mark Turner. (1998). Conceptual Integra- tion Networks. Cognitive Science, 22(2):133–187.

Gilles Fauconnier and Mark Turner. (2002). The Way We Think. Conceptual Blending and the Mind's Hidden Complexities. Basic Books.

Fellbaum, C. (ed.) 2008. WordNet: An Electronic Lexical Database. MIT Press, Cambridge.

Gentner, D. 1983, Structure-mapping: A Theoretical Framework. Cognitive Science 7:155–170.

Hearst, M. 1992. Automatic acquisition of hyponyms from large text corpora. In Proc. of the 14th International Conference on Computational Linguistics, pp 539–545.

Lakoff, G. and Johnson, M. 1980. Metaphors we live by. University of Chicago Press.

Pasca, M. and Van Durme, B. 2007. What You Seek is What You Get: Extraction of Class Attributes from Query Logs. In Proceedings of IJCAI-07, the 20th International Joint Conference on Artificial Intelligence.

Pereira, F. C. 2007. Creativity and artificial intelligence: a conceptual blending approach. Walter de Gruyter.

Seco, N., Veale, T. and Hayes, J. 2004. An Intrinsic Information Content Metric for Semantic Similarity in WordNet. In the proceedings of ECAI 2004, the 16th European Conference on Artificial Intelligence. Valencia, Spain. John Wiley

Shutova, E. 2010. Metaphor Identification Using Verb and Noun Clustering. In Proceedings of the 23rd International Conference on Computational Linguistics, 1001-1010.

Turney, P.D. and Littman, M.L. 2005. Corpus-based learning of analogies and semantic relations. Machine Learning 60(1-3):251- 278.

Veale, T. and D. O’Donoghue. (2000). Computation and Blending. Cognitive Linguistics, 11(3-4):253-281.

Veale, T. 2006. Tracking the Lexical Zeitgeist with Wikipedia and WordNet. Proceedings of ECAI-2006, the 17th European Conference on Artificial Intelligence.

Veale, T. and Hao, Y. 2007a. Making Lexical Ontologies Functional and Context-Sensitive. In Proceedings of the 46th Ann. Meeting of Assoc. of Computational Linguistics.

Veale T. and Hao, Y. 2007b. Comprehending and generating apt metaphors: a web-driven, case-based approach to figurative language. In Proceedings of AAAI’2007, the 22nd national conference on Artificial intelligence, pp.1471-1476.

Veale, T. and Li, G. 2011. Creative Introspection and Knowledge Acquisition: Learning about the world thru introspective questions and exploratory metaphors. In Proc. of AAAI’2011, the 25th Conference of the Association for the Advancement of Artificial Intelligence.

Veale, T. 2012 Exploding the Creativity Myth: The Computational Foundations of Linguistic Creativity. London: Bloomsbury/Continuum.


Full Text

intern file

Sonstige Links