  We consider open domain event extraction, the task of extracting unconstraint
types of events from news clusters. A novel latent variable neural model is
constructed, which is scalable to very large corpus. A dataset is collected and
manually annotated, with task-specific evaluation metrics being designed.
Results show that the proposed unsupervised model gives better performance
compared to the state-of-the-art method for event schema induction.
