Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation

Xiao Liu, Zhunchen Luo, Heyan Huang


Abstract
Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.
Anthology ID:
D18-1156
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1247–1256
Language:
URL:
https://aclanthology.org/D18-1156
DOI:
10.18653/v1/D18-1156
Bibkey:
Cite (ACL):
Xiao Liu, Zhunchen Luo, and Heyan Huang. 2018. Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1247–1256, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation (Liu et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1156.pdf
Video:
 https://vimeo.com/305198933
Code
 lx865712528/JMEE +  additional community code
Data
ACE 2005