Opposition theory and computational semiotics

  • Dan Assaf Independent researcher, Petach Tikva
  • Yochai Cohen Gilasio Coding Ltd., Tel-Aviv
  • Marcel Danesi University of Toronto, Toronto
  • Yair Neuman Department of Education, Ben-Gurion University of the Negev
Keywords: opposition theory, computational semiotics, metaphor identification

Abstract

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.
Published
2015-11-30
Section
Articles

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