A Reinforcement Learning-Based Generative Approach for Event Temporal Relation Extraction
Event temporal relation extraction is a crucial task in natural language processing, aimed at recognizing the temporal relations between event triggers in a text. Despite extensive efforts in this area, the existing methods face two main issues. Firstly, the previous models for event temporal relati...
Saved in:
| Main Authors: | Zhonghua Wu, Wenzhong Yang, Meng Zhang, Fuyuan Wei, Xinfang Liu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/27/3/284 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Applications of Multi-Robotic Arms to Assist Agricultural Production: A Review
by: Xiaojian Gai, et al.
Published: (2025-06-01) -
Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control with Spatio-Temporal Attention Mechanism
by: Wenzhe Jia, et al.
Published: (2025-08-01) -
ProLinker–Generator: Design of a PROTAC Linker Base on a Generation Model Using Transfer and Reinforcement Learning
by: Yanlin Luo, et al.
Published: (2025-05-01) -
TPVis: A Temporal Path Visualization System for Intuitive Understanding of Information Diffusion Inside Temporal Networks
by: Jincheol Oh, et al.
Published: (2025-01-01) -
A dual scheduling framework for task and resource allocation in clouds using deep reinforcement learning
by: Jiahui Pan, et al.
Published: (2025-06-01)