Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism

Shirong Shen, Guilin Qi, Zhen Li, Sheng Bi, Lusheng Wang


Abstract
Event extraction plays an important role in legal applications, including case push and auxiliary judgment. However, traditional event structure cannot express the connections between arguments, which are extremely important in legal events. Therefore, this paper defines a dynamic event structure for Chinese legal events. To distinguish between similar events, we design hierarchical event features for event detection. Moreover, to address the problem of long-distance semantic dependence and anaphora resolution in argument classification, we propose a novel pedal attention mechanism to extract the semantic relation between two words through their dependent adjacent words. We label a Chinese legal event dataset and evaluate our model on it. Experimental results demonstrate that our model can surpass other state-of-the-art models.
Anthology ID:
2020.coling-main.9
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
100–113
Language:
URL:
https://aclanthology.org/2020.coling-main.9
DOI:
10.18653/v1/2020.coling-main.9
Bibkey:
Cite (ACL):
Shirong Shen, Guilin Qi, Zhen Li, Sheng Bi, and Lusheng Wang. 2020. Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism. In Proceedings of the 28th International Conference on Computational Linguistics, pages 100–113, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism (Shen et al., COLING 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.9.pdf