Open Domain Event Extraction Using Neural Latent Variable Models
arXiv:1906.06947 · doi:10.18653/v1/P19-1276
Abstract
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.
arXiv commentaccepted by ACL 2019
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- ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data
- Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding