Open Domain Event Extraction Using Neural Latent Variable Models

Xiao Liu, Heyan Huang, Yue Zhang


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P19-1276.pdf
Video:
 https://aclanthology.org/P19-1276.mp4
Code
 lx865712528/ACL2019-ODEE