Learning Scripts as Hidden Markov Models

John Walker Orr, Prasad Tadepalli, Janardhan Rao Doppa, Xiaoli Z. Fern, Thomas G. Dietterich

AAAI 2014 pp. 1565-1571

doi:10.1609/AAAI.V28I1.8940 /aaai/2014/orr2014aaai-learning/

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 fillinggaps in the narratives and resolving ambiguous references. This paper proposes the first formal frameworkfor 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 andparameter learning based on Expectation Maximizationand 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 partialobservation sequences.

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Cite

Text

Orr et al. "Learning Scripts as Hidden Markov Models." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.8940

Markdown

[Orr et al. "Learning Scripts as Hidden Markov Models." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/orr2014aaai-learning/) doi:10.1609/AAAI.V28I1.8940

BibTeX

@inproceedings{orr2014aaai-learning,
  title     = {{Learning Scripts as Hidden Markov Models}},
  author    = {Orr, John Walker and Tadepalli, Prasad and Doppa, Janardhan Rao and Fern, Xiaoli Z. and Dietterich, Thomas G.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2014},
  pages     = {1565-1571},
  doi       = {10.1609/AAAI.V28I1.8940},
  url       = {https://mlanthology.org/aaai/2014/orr2014aaai-learning/}
}