TY - GEN
T1 - Open domain event extraction using neural latent variable models
AU - Liu, Xiao
AU - Huang, Heyan
AU - Zhang, Yue
N1 - Publisher Copyright:
© 2019 Association for Computational Linguistics.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85084079826&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85084079826
T3 - ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
SP - 2860
EP - 2871
BT - ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
PB - Association for Computational Linguistics (ACL)
T2 - 57th Annual Meeting of the Association for Computational Linguistics, ACL 2019
Y2 - 28 July 2019 through 2 August 2019
ER -