[Read full-text: Evaluating the premises of metaphor identification systems]
This study first examines the implicit and explicit premises of four systems for identifying metaphoric utterances from unannotated input text. All four systems are then evaluated on a common data set in order to see which premises are most successful. The goal is to see if these systems can find metaphors in a corpus that is mostly non-metaphoric without over-identifying literal and humorous utterances as metaphors. Three of the systems are distributional semantic systems, including a source-target mapping method; a word abstractness measurement method; and a semantic similarity measurement method. The fourth is a knowledge-based system which uses a domain interaction method based on the SUMO ontology, implementing the hypothesis that metaphor is a product of the interactions among all of the concepts represented in an utterance.