Current Semantic Role Labeling technolo- gies are based on inductive algorithms trained over large scale repositories of annotated examples. Frame-based sys- tems currently make use of the FrameNet database but fail to show suitable general- ization capabilities in out-of-domain sce- narios. In this paper, a state-of-art system for frame-based SRL is extended through the encapsulation of a distributional model of semantic similarity. The resulting argu- ment classification model promotes a sim- pler feature space that limits the potential overfitting effects. The large scale em- pirical study here discussed confirms that state-of-art accuracy can be obtained for out-of-domain evaluations.

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