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Open Domain Event Extraction Using Neural Latent Variable Models

arXiv 1906.06947 ↗First posted 17 Jun 2019cs.CLcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jun 2019.

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Citing papers per year: 2021: 6; 2022: 3; 2023: 2; 2024: 1; 2025: 3; 2026: 2.

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