Conceptual metaphor theory meets the data: a corpus-based human annotation study

Abstract

Metaphor makes our thoughts more vivid and fills our communication with richer imagery. Furthermore, according to the conceptual metaphor theory (CMT) of Lakoff and Johnson (Metaphors we live by. University of Chicago Press, Chicago, 1980), metaphor also plays an important structural role in the organization and processing of conceptual knowledge. According to this account, the phenomenon of metaphor is not restricted to similarity-based extensions of meanings of individual words, but instead involves activating fixed mappings that reconceptualize one whole area of experience in terms of another. CMT produced a significant resonance in the fields of philosophy, linguistics, cognitive science and artificial intelligence and still underlies a large proportion of modern research on metaphor. However, there has to date been no comprehensive corpus-based study of conceptual metaphor, which would provide an empirical basis for evaluating the CMT using real-world linguistic data. The annotation scheme and the empirical study we present in this paper is a step towards filling this gap. We test our annotation procedure in an experimental setting involving multiple annotators and estimate their agreement on the task. The goal of the study is to investigate (1) how intuitive the conceptual metaphor explanation of linguistic metaphors is for human annotators and whether it is possible to consistently annotate interconceptual mappings; (2) what are the main difficulties that the annotators experience during the annotation process; (3) whether one conceptual metaphor is sufficient to explain a linguistic metaphor or whether a chain of conceptual metaphors is needed. The resulting corpus annotated for conceptual mappings provides a new, valuable dataset for linguistic, computational and cognitive experiments on metaphor.