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.- Anthology ID:
- P19-1276
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2860–2871
- Language:
- URL:
- https://aclanthology.org/P19-1276
- DOI:
- 10.18653/v1/P19-1276
- Cite (ACL):
- Xiao Liu, Heyan Huang, and Yue Zhang. 2019. Open Domain Event Extraction Using Neural Latent Variable Models. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2860–2871, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Open Domain Event Extraction Using Neural Latent Variable Models (Liu et al., ACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/P19-1276.pdf
- Code
- lx865712528/ACL2019-ODEE