2019
ACL
ACL 2019
Open Domain Event Extraction Using Neural Latent Variable Models
Abstract
AbstractWe 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.
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Interdisciplinary Bridge
- Artificial Intelligence and Natural Language Processing
Authors
Topics
Machine Learning > Learning Types > Unsupervised Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Deep Learning > Models > Generative Models
Natural Language Processing > Applications > Information Extraction
Deep Learning > Learning Types > Self-Supervised Learning
Deep Learning > Learning Types > Unsupervised Learning