Statistical Script Learning with Multi-Argument Events
Karl Pichotta, Raymond J. Mooney · 2014
Scripts represent knowledge of stereotypical event sequences that can aid text understanding.Initial statistical methods have been developed to learn probabilistic scripts from raw text corpora; however, they utilize a very impoverished representation of events, consisting of a verb and one dependent argument.We present a script learning approach that employs events with multiple arguments.Unlike previous work, we model the interactions between multiple entities in a script.Experiments on a large corpus using the task of inferring held-out events (the "narrative cloze evaluation") demonstrate that modeling multi-argument events improves predictive accuracy.