Abstract
Event knowledge represents the knowledge of causal and temporal relations between events. Shared arguments of event knowledge encode patterns of role shifting in successive events. A two-stage framework was proposed for the task of Japanese event knowledge acquisition, in which related event pairs are first extracted, and shared arguments are then identified to form the complete event knowledge. This paper focuses on the second stage of this framework, and proposes a method to improve the shared argument identification of related event pairs. We constructed a gold dataset for shared argument learning. By evaluating our system on this gold dataset, we found that our proposed model outperformed the baseline models by a large margin.- Anthology ID:
- W17-2704
- Volume:
- Proceedings of the Events and Stories in the News Workshop
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Tommaso Caselli, Ben Miller, Marieke van Erp, Piek Vossen, Martha Palmer, Eduard Hovy, Teruko Mitamura, David Caswell
- Venue:
- EventStory
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 21–30
- Language:
- URL:
- https://aclanthology.org/W17-2704
- DOI:
- 10.18653/v1/W17-2704
- Cite (ACL):
- Yin Jou Huang and Sadao Kurohashi. 2017. Improving Shared Argument Identification in Japanese Event Knowledge Acquisition. In Proceedings of the Events and Stories in the News Workshop, pages 21–30, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Improving Shared Argument Identification in Japanese Event Knowledge Acquisition (Huang & Kurohashi, EventStory 2017)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/W17-2704.pdf