Mark Last

Metaphor identification in text is an open problem in natural language processing. In this paper, we present a new, supervised learning approach called MIL (Metaphor Identification by Learning), for identifying three major types of metaphoric expressions without using any knowledge resources or handcrafted rules. We derive a set of statistical features from a corpus representing a given domain (e.g., news articles published by Reuters). We also use an annotated set of sentences, which contain candidate expressions labelled as 'metaphoric' or 'literal' by native English speakers. Then we induce a metaphor identification model for each expression type by applying a classification algorithm to the set of annotated expressions. The proposed approach is evaluated on a set of annotated sentences extracted from a corpus of Reuters articles. We show a significant improvement vs. a state-of-the-art learning-based algorithm and comparable results to a recently presented rule-based approach.