Paper: Measuring metaphoricity

ACL ID P14-2121
Title Measuring metaphoricity
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

This paper presents the first computationally-derived scalar mea- surement of metaphoricity. Each input sentence is given a value between 0 and 1 which represents how metaphoric that sentence is. This measure achieves a correlation of 0.450 (Pearson?s R, p <0.01) with an experimental measure of metaphoricity involving human partici- pants. While far from perfect, this scalar measure of metaphoricity allows different thresholds for metaphoricity so that metaphor identification can be fitted for specific tasks and datasets. When reduced to a binary classification evaluation using the VU Amsterdam Metaphor Corpus, the system achieves an F-Measure of 0.608, slightly lower than the comparable binary classification system?s 0.638 and competitive with existing approaches.