# Open Domain Event Extraction Using Neural Latent Variable Models

arXiv: https://arxiv.org/abs/1906.06947 · HTML: https://mlsanity.com/paper/1906.06947/

## Key facts

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

## Built on by (most cited first, 17 of 17)

- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [Corpus-based Open-Domain Event Type Induction](https://mlsanity.com/paper/2109.03322/) (2021)
- [Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection](https://mlsanity.com/paper/2312.11891/) (2023)
- [A Survey on Deep Learning Event Extraction: Approaches and Applications](https://mlsanity.com/paper/2107.02126/) (2021)
- [Open-Vocabulary Argument Role Prediction for Event Extraction](https://mlsanity.com/paper/2211.01577/) (2022)
- [Harvesting Event Schemas from Large Language Models](https://mlsanity.com/paper/2305.07280/) (2023)
- [Event Extraction by Associating Event Types and Argument Roles](https://mlsanity.com/paper/2108.10038/) (2021)
- [An overview of event extraction and its applications](https://mlsanity.com/paper/2111.03212/) (2021)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention](https://mlsanity.com/paper/2202.05943/) (2022)
- [LegalCore: A Dataset for Event Coreference Resolution in Legal Documents](https://mlsanity.com/paper/2502.12509/) (2025)
- [Forecasting Crude Oil Price Using Event Extraction](https://mlsanity.com/paper/2111.09111/) (2021)
- [SEOE: A Scalable and Reliable Semantic Evaluation Framework for Open Domain Event Detection](https://mlsanity.com/paper/2503.03303/) (2025)
- [EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain](https://mlsanity.com/paper/2406.14075/) (2024)
- [Extracting Space Situational Awareness Events from News Text](https://mlsanity.com/paper/2201.05721/) (2022)
- [A Multimodal Text- and Graph-Based Approach for Open-Domain Event Extraction from Documents](https://mlsanity.com/paper/2604.21885/) (2026)
- [EVENT5Ws: A Large Dataset for Open-Domain Event Extraction from Documents](https://mlsanity.com/paper/2604.21890/) (2026)

## 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.