Gen-Meta: Generating metaphors by combining AI and corpus-based modeling

Data fusion and abductive inference for metaphor resolution: a bridging discussion

Since the 1980s, metaphor has been recognized as a pervasively diffused phenomenon in communication, absolutely not restricted to rhetoric and linguistic phenomena, involving structured concepts, relations, and matching ‘rules’. Metaphor resolution, that is metaphor understanding, as well as metaphor creation, has become an issue in automated processing and understanding of natural language as well as of mixed visual communication. It can be showed as a process of structure finding and mapping procedure between conceptual denotation–connotation structures necessary for interpretation. Creative abduction is then showed to be the pattern inference required to work out structure-mappings in corresponding nodes as present in metaphors. In this paper, we review some key issues (definitions, typologies, theoretical problems) involving the concept of ‘metaphor’ and survey some definitions and concepts emerging in contemporary debate on abductive inference. Finally, we argue that metaphor understanding process can be recognized as a fusion tractable problem, allowing the exploitation of frameworks and algorithms of such domain.