Tutorial: Extracting Rich Event Structure from Text (models and evaluations)

This tutorial will introduce and review current research on learning event schemas from text with minimal human supervision. There have been several lines of work in the past 5-10 years that focus on learning broader event schemas from text. Scehas are a generalized representation of the events and entities that make up a typical scenario in the world. For instance, atomic events like sneeze, take your temperature, visit a doctor, and fill a prescription are all inherently related to a broader 'illness scenario' that humans naturally understand. Doctors, medicine, and symptoms are all entities that fill particular roles in the series of events. These were famously called scripts in the 1970's, but most recently, several lines of research have focused on new computational methods to learn scripts. As is usual, many methods have been proposed, and different event representations and learning approaches have been applied.

This tutorial has four main goals: (1) generally expose the audience to script/schema research in recent years, including both formal and informal probabilistic models, (2) focus in depth on generative models to the unsupervised learning task, (3) give a practical overview of working code for one such generative model, and (4) review the diverse set of evaluations of schema knowledge.