FrameNet data enabled the development of wide-coverage frame parsers using supervised learning (Gildea and Jurafsky, 2002;Erk and Padó, 2006;Das et al., 2014, inter alia), as well as its application to a wide range of tasks, ranging from answer extraction in Question Answering (Shen and Lapata, 2007) and Textual Entailment (Burchardt et al., 2009;Ben Aharon et al., 2010).
frame-semantic resources are arguably expensive and time-consuming to build due to difficulties in defining the frames, their granularity and domain, as well as the complexity of the construction and annotation tasks requiring expertise in the underlying knowledge.