Mixing Metaphors

TL;DR

This paper argues that mixed metaphors are an important and complex aspect of metaphorical reasoning that has been overlooked, and demonstrates their significance within an AI reasoning framework.

Contribution

It provides an analysis of mixed metaphors and emphasizes their importance for comprehensive metaphor theories within AI systems.

Findings

Mixed metaphors require similar reasoning as straight metaphors.

Mixing is central to understanding metaphorical meaning.

AI systems can reason about both parallel and serial mixed metaphors.

Abstract

Mixed metaphors have been neglected in recent metaphor research. This paper suggests that such neglect is short-sighted. Though mixing is a more complex phenomenon than straight metaphors, the same kinds of reasoning and knowledge structures are required. This paper provides an analysis of both parallel and serial mixed metaphors within the framework of an AI system which is already capable of reasoning about straight metaphorical manifestations and argues that the processes underlying mixing are central to metaphorical meaning. Therefore, any theory of metaphors must be able to account for mixing.