Verifying Claims About Metaphors with Large-Scale Automatic Metaphor Identification
This work addresses a gap in computational linguistics by empirically testing theoretical claims about metaphor usage, though it is incremental as it applies existing methods to new data.
The study tackled the problem of verifying linguistic claims about verb metaphors using large-scale corpus analysis, finding that metaphors are associated with less concrete, imageable, and familiar direct objects and are more common in emotional and subjective sentences.
There are several linguistic claims about situations where words are more likely to be used as metaphors. However, few studies have sought to verify such claims with large corpora. This study entails a large-scale, corpus-based analysis of certain existing claims about verb metaphors, by applying metaphor detection to sentences extracted from Common Crawl and using the statistics obtained from the results. The verification results indicate that the direct objects of verbs used as metaphors tend to have lower degrees of concreteness, imageability, and familiarity, and that metaphors are more likely to be used in emotional and subjective sentences.