Open Domain Event Extraction Using Neural Latent Variable Models
This addresses the problem of extracting unconstrained event types from large-scale news data for applications in information retrieval and analysis.
The paper tackles open domain event extraction from news clusters by proposing a novel latent variable neural model, which achieves better performance than the state-of-the-art method for event schema induction.
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.