Distributional semantic features in Russian verbal metaphor identification.
Our experiment is aimed at evaluating the performance of distributional semantic
features in metaphor identification in Russian raw text. We apply two
types of distributional features representing similarity between the metaphoric/
literal verb and its syntactic or linear context. Our approach is evaluated
on a dataset of nine Russian verb context, which is made available
to the community. The results show that both sets of similarity features are
useful for metaphor identification, and do not replicate each other, as their
combination systematically improves the performance for individual verb
sense classification, reaching state-of-the-art results for verbal metaphor
identification. A combined verb classification demonstrates that the suggested
features effectively generalize over metaphoric usage in different
verbs, shows that linear coherence features perform as well as the combined
feature approach. By analyzing the errors we conclude that syntactic
parsing quality is still modest for raw-text metaphor identification in Russian,
and discuss properties of semantic models required for high performance.