Learning Scripts as Hidden Markov Models
This provides a robust inference and learning method for narrative analysis, but it is incremental as it builds on existing clustering models.
The paper tackles the problem of modeling stereotypical event sequences in narratives by proposing the first formal framework for scripts based on Hidden Markov Models, with results showing superiority over baselines in predicting missing events.
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.