A Tiered Approach to the Recognition of Metaphor

We present a tiered-approach to the recognition of metaphor. The first tier is made up of highly precise expert-driven lexico-syntactic patterns which are automatically expended on in the second tier using lexical and dependency transformations. The final tier utilizes an SVM classifier using a variety of syntactic, semantic, and psycholinguistic features to determine if an expression is metaphoric. We focus on the recognition of metaphors in which the target is associated with the concept of \"Economic Inequality\" and examine the effectiveness of our approach for metaphors expressed in English, Farsi, Russian, and Spanish. Through experimental analysis we show that the proposed approach is capable of achieving 67.4% to 77.8% F-Measure depending on the language.