Xiaoyang Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Xiaoyang Wang has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Wenjie Luo
Hebei University
1 shared publication - Xiudan Yang
School of Management, Hebei University, Baoding 071000, China
1 shared publication
Latest Publications
- Open Access
- 7 January 2025
- Published by MDPI AG in Information
- Vol. 16 (1) , 32
An Event Causality Identification Framework Using Ensemble Learning
Event causality identification is an upstream operation for many tasks, including knowledge graphs and intelligent question-and-answer systems. The latest models introduce external knowledge and then use deep learning for causality prediction. However, event causality recognition still faces problems such as data imbalance and insufficient event content richness. Additionally, previous frameworks have utilized a single model, but these frequently produce unsatisfactory outcomes such as lower precision rates and lower recall rates. We propose the concept of ensemble learning, which combines multiple models to achieve frameworks that perform as well as or better than the latest models. This framework combines the advantages of Mamba, a temporal convolutional network, and graph computation to identify event causality more effectively and accurately. After comparing our framework to standard datasets, our F1-scores (measures of model accuracy) are essentially the same as those of the state-of-the-art (SOTA) methods on one dataset.
- 7 January 2025
- Published by MDPI AG in Information
- Vol. 16 (1) , 32
Open AccessAn Event Causality Identification Framework Using Ensemble Learning
Event causality identification is an upstream operation for many tasks, including knowledge graphs and intelligent question-and-answer systems. The latest models introduce external knowledge and then use deep learning for causality prediction. However, event causality recognition still faces problems such as data imbalance and insufficient event content richness. Additionally, previous frameworks have utilized a single model, but these frequently produce unsatisfactory outcomes such as lower precision rates and lower recall rates. We propose the concept of ensemble learning, which combines multiple models to achieve frameworks that perform as well as or better than the latest models. This framework combines the advantages of Mamba, a temporal convolutional network, and graph computation to identify event causality more effectively and accurately. After comparing our framework to standard datasets, our F1-scores (measures of model accuracy) are essentially the same as those of the state-of-the-art (SOTA) methods on one dataset.