@article{Assaf_Cohen_Danesi_Neuman_2015, title={Opposition theory and computational semiotics}, volume={43}, url={https://ojs.utlib.ee/index.php/sss/article/view/SSS.2015.43.2-3.01}, DOI={10.12697/SSS.2015.43.2-3.01}, abstractNote={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.}, number={2/3}, journal={Sign Systems Studies}, author={Assaf, Dan and Cohen, Yochai and Danesi, Marcel and Neuman, Yair}, year={2015}, month={Nov.}, pages={159–172} }