Opposition theory and computational semiotics

Dan Assaf, Yochai Cohen, Marcel Danesi, Yair Neuman

Research output: Contribution to journalArticlepeer-review


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 adjectivenoun 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.

Original languageEnglish
Pages (from-to)159-172
Number of pages14
JournalSign Systems Studies
Issue number2-3
StatePublished - 1 Jan 2015


  • Computational semiotics
  • Metaphor identification
  • Opposition theory

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language


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