@inproceedings{liu-etal-2019-open,
    title = "Open Domain Event Extraction Using Neural Latent Variable Models",
    author = "Liu, Xiao  and
      Huang, Heyan  and
      Zhang, Yue",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/P19-1276/",
    doi = "10.18653/v1/P19-1276",
    pages = "2860--2871",
    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."
}