Identifying Metaphoric Antonyms in a Corpus Analysis of Finance Articles
Abstract
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 participants identified antonyms of these verbs in a free-response task and a matching task from which 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. 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 first- or second-most similar pair in the set of alternatives. The implications of these results for distributional approaches to determining metaphoric knowledge are discussed.
- Publication:
-
arXiv e-prints
- Pub Date:
- December 2012
- DOI:
- 10.48550/arXiv.1212.3139
- arXiv:
- arXiv:1212.3139
- Bibcode:
- 2012arXiv1212.3139G
- Keywords:
-
- Computer Science - Computation and Language
- E-Print:
- arXiv admin note: text overlap with arXiv:1212.3138