Methods for automatic detection and interpretation of metaphors have focused on analysis and utilization of the ways in which metaphors violate selectional preferences (Martin, 2006). Detection and interpretation processes that rely on this method can achieve wide coverage and may be able to detect some novel metaphors. However, they are prone to high false alarm rates, often arising from imprecision in parsing and supporting ontological and lexical resources. An alternative approach to metaphor detection emphasizes the fact that many metaphors become conventionalized collocations, while still preserving their active metaphorical status. Given a large enough corpus for a given language, it is possible to use tools like SketchEngine (Kilgariff, Rychly, Smrz, & Tugwell, 2004) to locate these high frequency metaphors for a given target domain. In this paper, we examine the application of these two approaches and discuss their relative strengths and weaknesses for metaphors in the target domain of economic inequality in English, Spanish, Farsi, and Russian.