Identifying Metaphoric Antonyms in a Corpus Analysis of Finance Articles

Using a corpus of 17,000+ financial news reports (involving over 10M words), we perform an analysis of the argument-distributions of the UP and DOWN-verbs used to describe movements of indices, stocks and shares. In Study 1 people identified antonyms of these verb sets in a free-generation task and a match-theopposite task and the most commonly identified antonyms were compiled. In Study 2, we determined whether the argument-distributions for the verbs in these antonym-pairs were sufficiently similar to predict the most frequently-identified antonym. It was found that cosine similarity correlates moderately with the proportions of antonym-pairs identified by people (r = 0.31). More impressively, 87% of the time the most frequently-identified antonym is either the firstor second-most similar pair in the set of alternatives. The implications of these results for distributional approaches to determining metaphoric knowledge are discussed.