Learning Semantic Script Knowledge with Event Embeddings

Induction of common sense knowledge about prototypical sequences of events has recently re-ceived much attention (e.g., (Chambers & Juraf-sky, 2008; Regneri et al., 2010)). Instead of in-ducing this knowledge in the form of graphs, as in much of the previous work, in our method, dis-tributed representations of event realizations are computed based on distributed representations of predicates and their arguments, and then these representations are used to predict prototypical event orderings. The parameters of the com-positional process for computing the event rep-resentations and the ranking component of the model are jointly estimated from texts. We show that this approach results in a substantial boost in ordering performance with respect to previous methods. 1