Most current approaches to metaphor identi-fication use restricted linguistic contexts, e.g.by considering only a verb’s arguments or the sentence containing a phrase.Inspired by pragmatic accounts of metaphor, we argue that broader discourse features are crucial for bet-ter metaphor identification. We train simple gradient boosting classifiers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods, obtaining near state-of-the-art results on the 2018 VU Amsterdam metaphor iden-tification task without the complex metaphor-specific features or deep neural architectures employed by other systems.A qualitative analysis further confirms the need for broader context in metaphor processing.