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5 - Event Causality

from Part One - Foundational Components of Storylines

Published online by Cambridge University Press:  06 November 2021

Tommaso Caselli
Affiliation:
University of Groningen
Eduard Hovy
Affiliation:
Carnegie Mellon University, Pennsylvania
Martha Palmer
Affiliation:
University of Colorado Boulder
Piek Vossen
Affiliation:
Vrije Universiteit, Amsterdam
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Summary

A crucial aspect of understanding and reconstructing narratives is identifying the underlying causal chains, which explain why certain things happened and make a coherent story. To build such causal chains, we need to identify causal links between events in the story, which may be expressed explicitly as well as understood implicitly using commonsense knowledge.

This chapter reviews research efforts on the automated extraction of such event causality from natural language text. It starts with a brief review of existing causal models in psychology and psycholinguistics as a building block for understanding causation. These models are useful tools for guiding the annotation process to build corpora annotated with causal pairs. I then outline existing annotated resources, which are used to build and evaluate automated causality extraction systems. Furthermore, circumstantial events surrounding the causal complex are rarely expressed with language as they are part of common sense knowledge. Therefore, discovering causal common sense is also important to fill the gaps in the causal chains, and I discuss existing work in this line of research.

Type
Chapter
Information
Computational Analysis of Storylines
Making Sense of Events
, pp. 106 - 124
Publisher: Cambridge University Press
Print publication year: 2021

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