  Scripts have been proposed to model the stereotypical event sequences found
in narratives. They can be applied to make a variety of inferences including
filling gaps in the narratives and resolving ambiguous references. This paper
proposes the first formal framework for scripts based on Hidden Markov Models
(HMMs). Our framework supports robust inference and learning algorithms, which
are lacking in previous clustering models. We develop an algorithm for
structure and parameter learning based on Expectation Maximization and evaluate
it on a number of natural datasets. The results show that our algorithm is
superior to several informed baselines for predicting missing events in partial
observation sequences.
