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What makes a good pun : a corpus analysis based on metacomments

Authors :
Lundmark, Carita
Lundmark, Carita
Publication Year :
2015

Abstract

This paper was inspired by a study of mixed metaphor as a meta-linguistic comment (Semino 2015) and builds on a previous conference presentation based on the 176 instances of the word pun preceded by an adjective in the Corpus of Contemporary American English (COCA), totalling 450M words. The data for the present study includes all 784 instances of pun in the corpus, and the material is analysed with respect to the characteristics of the pun, i.e. how the ambiguity is created (metaphor, metonymy, homonymy etc.) and what type of scenarios are invoked by the two senses. This is then compared to the quality of the pun as expressed in the metacomment. In a pun, two meanings are incongruously combined in the same utterance (e.g. Ross 1998: 8), in cognitive linguistic terms invoking two scenarios or mental spaces that can either be very detailed or fairly schematic. Theoretically, the study shows how a more specific level of abstraction often is involved in conceptualisation processes, as suggested by Johansson Falck (2013), “making the schema more concrete and easier to refer to” (2013: 216), and enabling the construction of a blended space by providing two input spaces that are rich enough to share a generic space and allow cross-space mappings (Fauconnier & Turner 2002). The present study builds on the idea that puns, like mixed metaphors, display a sensitivity to “specific scenarios rather than broad source domains” (Semino 2015: 28), and further explores the earlier tentative conclusion that quality does not seem to be related to how the ambiguity is created, but to whether there is a meaningful connection between the two scenarios. A good-quality pun seems easier to achieve if certain types of scenarios are involved by virtue of the context.

Details

Database :
OAIster
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1233976074
Document Type :
Electronic Resource