Document Type
Article
Publication Date
2018
Abstract
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
Recommended Citation
Orr, J. Walker; Tadepalli, Prasad; Doppa, Janardhan Rao; Fern, Xiaoli; and Dietterich, Thomas, "Learning Scripts as Hidden Markov Models" (2018). Faculty Publications - Department of Electrical Engineering and Computer Science. 46.
https://digitalcommons.georgefox.edu/eecs_fac/46
Comments
Orr, J. W., Prasad Tadepalli, Rao Doppa, J., Fern, X., & Dietterich, T. G. (2018). Learning Scripts as Hidden Markov Models (1809.03680v1). arXiv. https://arxiv.org/abs/1809.03680v1