# Learning Scripts as Hidden Markov Models

arXiv: https://arxiv.org/abs/1809.03680 · HTML: https://mlsanity.com/paper/1809.03680/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [Learning Hidden Markov Models When the Locations of Missing Observations are Unknown](https://mlsanity.com/paper/2203.06527/) (2022)
- [Probabilistic Systems with Hidden State and Unobservable Transitions](https://mlsanity.com/paper/2205.13871/) (2022)

## Abstract (arXiv)

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.