Given the wide body of work in frame-semantic analysis of text, and recent interest in using framesemantic parsers in NLP applications, the future directions of research look exciting. First and foremost, to improve the quality of automatic frame-semantic parsers, the coverage of the FrameNet lexicon on free English text, and the number of annotated targets needs to increase. For example, the training dataset used for the state-ofthe-art system of <cite>Hermann et al. (2014)</cite> contains only 4,458 labeled targets, which is approximately 40 times less than the number of annotated targets in Ontonotes 4.0 (Hovy et al., 2006) , a standard NLP dataset, containing PropBank-style verb annotations.