Finally, the attention-based bLSTM with Word2Vec features proved to be the most effective out of all the classifiers, with features from the skip-gram algorithm providing the best results of an F-score of 84.61.
depending on the degree to which the metaphor is conventionalized in the language, it is expected to be far more difficult to detect using computational methods, since its domain essentially becomes indistinguishable from the rest of the context.