AAAI Publications, Workshops at the Twenty-Sixth AAAI Conference on Artificial Intelligence

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Learning Sociocultural Knowledge via Crowdsourced Examples
Boyang Li, Darren Scott Appling, Stephen Lee-Urban, Mark Riedl

Last modified: 2012-07-15

Abstract


Computational systems can use sociocultural knowledge to understand human behavior and interact with humans in more natural ways. However, such systems are limited by their reliance on hand-authored sociocultural knowledge and models. We introduce an approach to automatically learn robust, script-like sociocultural knowledge from crowdsourced narratives. Crowdsourcing, the use of anonymous human workers, provides an opportunity for rapidly acquir­ing a corpus of examples of situations that are highly specialized for our purpose yet sufficiently varied, from which we can learn a versatile script. We describe a semi-automated process by which we query human workers to write natural language narrative examples of a given situation and learn the set of events that can occur and the typical even ordering.

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