Word embeddings and pre-trained language models have achieved great performance in many tasks due to their ability to capture both syntactic and semantic information in their representations. The vector space representations have also been used to identify figurative language shifts such as metaphors, however, the more recent contextualized models have mostly been evaluated via their performance on downstream tasks. In this article, we evaluate static and contextualized word embeddings in terms of their representation and unsupervised identification of relation-level (ADJ-NOUN, NOUN-NOUN) metaphors in Slovene on a set of 24 literal and 24 metaphorical phrases. Our experiments show very promising results for both embedding methods, however, the performance in contextual embeddings notably depends on the layer involved and the input provided to the model.

The study of metaphors in media discourse is an increasingly researched topic as media are an important shaper of social reality and metaphors are an indicator of how we think about certain issues through references to other things. We present a neural transfer learning method for detecting metaphorical sentences in Slovene and evaluate its performance on a gold standard corpus of metaphors (classification accuracy of 0.725), as well as on a sample of a domain specific corpus of migrations (precision of 0.40 for extracting domain metaphors and 0.74 if evaluated only on a set of migration related sentences). Based on empirical results and findings of our analysis, we propose a novel metaphor annotation scheme containing linguistic level, conceptual level, and stance information. The new scheme can be used for future metaphor annotations of other socially relevant topics.