Outsourcing FrameNet to the Crowd

We present the first attempt to perform full FrameNet annotation with crowdsourcing techniques. We compare two approaches: the first one is the standard annotation methodology of lexical units and frame elements in two steps, while the second is a novel approach aimed at acquiring frames in a bottom-up fashion, starting from frame element annotation. We show that our methodology, relying on a single annotation step and on simplified role definitions, outperforms the standard one both in terms of accuracy and time.