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Sign Systems Studies

Volume 43, Issue 2/3, 2015

Special Issue: Sign Evolution on Multiple Time Scales

Dan Assaf, Yochai Cohen, Marcel Danesi, Yair Neuman
Pages 159-172

Opposition theory and computational semiotics

Opposition theory suggests that binary oppositions (e.g., high vs. low) underlie basic cognitive and linguistic processes. However, opposition theory has never been implemented in a computational cognitive-semiotics model. In this paper, we present a simple model of metaphor identification that relies on opposition theory. An algorithm instantiating the model has been tested on a data set of 100 phrases comprising adjective-noun pairs in which approximately a half represent metaphorical language-use (e.g., dark thoughts) and the rest literal language-use (e.g., dark hair). The algorithm achieved 89% accuracy in metaphor identification and illustrates the relevance of opposition theory for modelling metaphor processing.

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