FrameNet has inspired a direction in NLP research known as semantic role labelling (Gildea and Jurafsky, 2002;Màrquez et al., 2008) and frame-semantic parsing (Das et al., 2014), whose goal is to assign semantic roles to arguments of the verbs in a sentence.
these works point out the coverage limitations of the hand-constructed FrameNet database, suggesting that a data-driven frame acquisition method is needed to enable the integration of frame semantics into real-world NLP applications.