Abstract
This paper tackles the task of event detection (ED), which involves identifying and categorizing events. We argue that arguments provide significant clues to this task, but they are either completely ignored or exploited in an indirect manner in existing detection approaches. In this work, we propose to exploit argument information explicitly for ED via supervised attention mechanisms. In specific, we systematically investigate the proposed model under the supervision of different attention strategies. Experimental results show that our approach advances state-of-the-arts and achieves the best F1 score on ACE 2005 dataset.- Anthology ID:
- P17-1164
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1789–1798
- Language:
- URL:
- https://aclanthology.org/P17-1164
- DOI:
- 10.18653/v1/P17-1164
- Cite (ACL):
- Shulin Liu, Yubo Chen, Kang Liu, and Jun Zhao. 2017. Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1789–1798, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Exploiting Argument Information to Improve Event Detection via Supervised Attention Mechanisms (Liu et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P17-1164.pdf
- Data
- FrameNet