Open Domain Event Extraction Using Neural Latent Variable Models
Computation and Language
2022-12-19 v1
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.
Cite
@article{arxiv.1906.06947,
title = {Open Domain Event Extraction Using Neural Latent Variable Models},
author = {Xiao Liu and Heyan Huang and Yue Zhang},
journal= {arXiv preprint arXiv:1906.06947},
year = {2022}
}
Comments
accepted by ACL 2019