Titov and Khoddam (2015) proposed a reconstruction-error minimization approach using a log-linear model to predict roles given syntactic and lexical features and a probabilistic tensor factorization model to identify argument fillers based on the role predictions and the predicate.
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.